3344 Big Data Jobs - Page 45

Responsibilities - Conduct feature engineering, data analysis, and data exploration to extract valuable insights. - Develop and optimize Machine Learning models to achieve high accuracy and performance. - Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness. - Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback. - Collaborate with cross-functional teams to align ML solutions with business goals. - Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models. - Bring in the latest advancements in ML and AI to drive innovation. Required Skills - 4-5 years of hands-on experience in Machine Learning and Deep Learning. - Strong expertise in feature engineering, data exploration, and data preprocessing. - Experience with imbalanced datasets and techniques to improve model generalization. - Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks. - Strong mathematical and statistical knowledge with problem-solving skills. - Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Skills - Experience with Big Data technologies (Hadoop, Spark, etc.) - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Experience in automating ML pipelines with MLOps practices. - Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

About the Role: We are seeking an experienced Data Engineering Architect to join our team. In this role, you will be responsible for designing comprehensive data architecture and lead the design and development of scalable, modern data platforms that power business-critical data products and insights. You will work closely with customers and technology partners to deliver data solutions that address complex telecommunications business requirements including customer experience management, operational analytics, digital transformation initiatives and AI/ML enablement. The ideal candidate will bring deep technical expertise across cloud data engineering, data lakehouse architecture, and modular, reusable data components and have strong hands-on experience with Azure, Databricks, Delta Lake, and modern data engineering tools and frameworks. Responsibilities: Design and implement robust, scalable, and cost-effective data architectures at enterprise level using the Azure Databricks Lakehouse platform. Architect modern Delta Lakehouse platforms to support structured and semi-structured data ingestion, processing, and analytics. Implement robust data integration and orchestration pipelines using platforms Kafka, ADF, Airflow, Event hubs etc. Experience in building Customer 360 platforms (CDP,CIH etc ) Create data architectures that support business-specific use cases including customer journey analytics, CLTV, churn score, market segment and enable reverse ETL. Collaborate with domain owners (CRM, billing ,usage) to define data contracts and model domain specific datasets. Lead the definition of data modeling standards (dimensional, normalized, data vault) and best practices. Establish and enforce data governance, security, and privacy controls aligned with regulatory compliance requirements like GDPR, PII etc. Collaborate with data engineers, product teams, business stakeholders and clients to translate business needs into scalable data solutions. Evaluate and recommend appropriate big data technologies, cloud platforms, and processing frameworks based on business-specific requirements and regulatory compliance needs. Stay current with the latest advancements in data technologies including cloud services, data processing frameworks, and AI/ML capabilities Contribute to the development of best practices, reference architectures, and reusable solution components for accelerating proposal development Qualifications: Bachelor's or Master's degree in Computer Science, Data Science, or a related technical field 10+ years of experience in data engineering, BI architecture with at least 5+ years in architectural or solution architecture Strong experience with: • Azure (Data Lake, Data Factory, Synapse, Key Vault, Azure AI services) • Databricks, Delta Lake, Delta live tables • PySpark,Scala,SQL, Python, Kafka Unity Catalog, Alation Familiarity with modern data frameworks and tools: • Apache Kafka, Airflow, Flink, NiFi, dbt, Iceberg Deep understanding of data lakehouse concepts, data modeling, and pipeline orchestration and performance optimization. Proven ability to design reusable, domain-driven data products in a large-scale enterprise environment Experience in data governance, metadata management, data cataloging, data quality, lineage and compliance. Exposure to data mesh, domain ownership models, and data product thinking. Understanding of DevOps, CI/CD in data environments. Knowledge of system monitoring and observability tools Prometheus and Grafana Experience designing and implementing data lakes, data warehouses, and machine learning pipelines for business use cases Excellent communication and presentation skills with ability to explain complex technical concepts to business stakeholders Good to have TMforum certifications or telecommunications industry certifications Relevant data platform certifications such as Databricks, Azure Data Engineer are a plus Willingness to travel as required

Responsibilities - Conduct feature engineering, data analysis, and data exploration to extract valuable insights.- Develop and optimize Machine Learning models to achieve high accuracy and performance.- Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness.- Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback.- Collaborate with cross-functional teams to align ML solutions with business goals.- Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models.- Bring in the latest advancements in ML and AI to drive innovation. Required Skills - 4-5 years of hands-on experience in Machine Learning and Deep Learning.- Strong expertise in feature engineering, data exploration, and data preprocessing.- Experience with imbalanced datasets and techniques to improve model generalization.- Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks.- Strong mathematical and statistical knowledge with problem-solving skills.- Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Skills - Experience with Big Data technologies (Hadoop, Spark, etc.)- Familiarity with containerization and orchestration tools (Docker, Kubernetes).- Experience in automating ML pipelines with MLOps practices.- Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Responsibilities - Conduct feature engineering, data analysis, and data exploration to extract valuable insights.- Develop and optimize Machine Learning models to achieve high accuracy and performance.- Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness.- Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback.- Collaborate with cross-functional teams to align ML solutions with business goals.- Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models.- Bring in the latest advancements in ML and AI to drive innovation. Required Skills - 4-5 years of hands-on experience in Machine Learning and Deep Learning.- Strong expertise in feature engineering, data exploration, and data preprocessing.- Experience with imbalanced datasets and techniques to improve model generalization.- Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks.- Strong mathematical and statistical knowledge with problem-solving skills.- Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Skills - Experience with Big Data technologies (Hadoop, Spark, etc.)- Familiarity with containerization and orchestration tools (Docker, Kubernetes).- Experience in automating ML pipelines with MLOps practices.- Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Key Responsibilities : - Conduct feature engineering, data analysis, and data exploration to extract valuable insights. - Develop and optimize Machine Learning models to achieve high accuracy and performance. - Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness. - Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback. - Collaborate with cross-functional teams to align ML solutions with business goals. - Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models. - Bring in the latest advancements in ML and AI to drive innovation. Requirements : - 4-5 years of hands-on experience in Machine Learning and Deep Learning. - Strong expertise in feature engineering, data exploration, and data preprocessing. - Experience with imbalanced datasets and techniques to improve model generalization. - Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks. - Strong mathematical and statistical knowledge with problem-solving skills. - Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Qualifications : - Experience with Big Data technologies (Hadoop, Spark, etc.) - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Experience in automating ML pipelines with MLOps practices. - Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Responsibilities - Conduct feature engineering, data analysis, and data exploration to extract valuable insights. - Develop and optimize Machine Learning models to achieve high accuracy and performance. - Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness. - Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback. - Collaborate with cross-functional teams to align ML solutions with business goals. - Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models. - Bring in the latest advancements in ML and AI to drive innovation. Required Skills - 4-5 years of hands-on experience in Machine Learning and Deep Learning. - Strong expertise in feature engineering, data exploration, and data preprocessing. - Experience with imbalanced datasets and techniques to improve model generalization. - Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks. - Strong mathematical and statistical knowledge with problem-solving skills. - Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Skills - Experience with Big Data technologies (Hadoop, Spark, etc.) - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Experience in automating ML pipelines with MLOps practices. - Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Key Responsibilities : - Conduct feature engineering, data analysis, and data exploration to extract valuable insights. - Develop and optimize Machine Learning models to achieve high accuracy and performance. - Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness. - Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback. - Collaborate with cross-functional teams to align ML solutions with business goals. - Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models. - Bring in the latest advancements in ML and AI to drive innovation. Requirements : - 4-5 years of hands-on experience in Machine Learning and Deep Learning. - Strong expertise in feature engineering, data exploration, and data preprocessing. - Experience with imbalanced datasets and techniques to improve model generalization. - Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks. - Strong mathematical and statistical knowledge with problem-solving skills. - Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Qualifications : - Experience with Big Data technologies (Hadoop, Spark, etc.) - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Experience in automating ML pipelines with MLOps practices. - Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Responsibilities - Conduct feature engineering, data analysis, and data exploration to extract valuable insights. - Develop and optimize Machine Learning models to achieve high accuracy and performance. - Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness. - Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback. - Collaborate with cross-functional teams to align ML solutions with business goals. - Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models. - Bring in the latest advancements in ML and AI to drive innovation. Required Skills - 4-5 years of hands-on experience in Machine Learning and Deep Learning. - Strong expertise in feature engineering, data exploration, and data preprocessing. - Experience with imbalanced datasets and techniques to improve model generalization. - Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks. - Strong mathematical and statistical knowledge with problem-solving skills. - Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Skills - Experience with Big Data technologies (Hadoop, Spark, etc.) - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Experience in automating ML pipelines with MLOps practices. - Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Key Responsibilities : - Conduct feature engineering, data analysis, and data exploration to extract valuable insights. - Develop and optimize Machine Learning models to achieve high accuracy and performance. - Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness. - Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback. - Collaborate with cross-functional teams to align ML solutions with business goals. - Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models. - Bring in the latest advancements in ML and AI to drive innovation. Requirements : - 4-5 years of hands-on experience in Machine Learning and Deep Learning. - Strong expertise in feature engineering, data exploration, and data preprocessing. - Experience with imbalanced datasets and techniques to improve model generalization. - Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks. - Strong mathematical and statistical knowledge with problem-solving skills. - Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Qualifications : - Experience with Big Data technologies (Hadoop, Spark, etc.) - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Experience in automating ML pipelines with MLOps practices. - Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Responsibilities - Conduct feature engineering, data analysis, and data exploration to extract valuable insights. - Develop and optimize Machine Learning models to achieve high accuracy and performance. - Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness. - Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback. - Collaborate with cross-functional teams to align ML solutions with business goals. - Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models. - Bring in the latest advancements in ML and AI to drive innovation. Required Skills - 4-5 years of hands-on experience in Machine Learning and Deep Learning. - Strong expertise in feature engineering, data exploration, and data preprocessing. - Experience with imbalanced datasets and techniques to improve model generalization. - Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks. - Strong mathematical and statistical knowledge with problem-solving skills. - Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Skills - Experience with Big Data technologies (Hadoop, Spark, etc.) - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Experience in automating ML pipelines with MLOps practices. - Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Location: Bangalore/Hyderabad/Pune Experience level: 7+ Years About the Role We are seeking a highly skilled Snowflake Developer to join our team in Bangalore. The ideal candidate will have extensive experience in designing, implementing, and managing Snowflake-based data solutions. This role involves developing data architectures and ensuring the effective use of Snowflake to drive business insights and innovation. Key Responsibilities: Design and implement scalable, efficient, and secure Snowflake solutions to meet business requirements. Develop data architecture frameworks, standards, and principles, including modeling, metadata, security, and reference data. Implement Snowflake-based data warehouses, data lakes, and data integration solutions. Manage data ingestion, transformation, and loading processes to ensure data quality and performance. Collaborate with business stakeholders and IT teams to develop data strategies and ensure alignment with business goals. Drive continuous improvement by leveraging the latest Snowflake features and industry trends. Qualifications: Bachelors or Masters degree in Computer Science, Information Technology, Data Science, or a related field. 8+ years of experience in data architecture, data engineering, or a related field. Extensive experience with Snowflake, including designing and implementing Snowflake-based solutions. Must have exposure working in Airflow Proven track record of contributing to data projects and working in complex environments. Familiarity with cloud platforms (e.g., AWS, GCP) and their data services. Snowflake certification (e.g., SnowPro Core, SnowPro Advanced) is a plus.

Responsibilities - Conduct feature engineering, data analysis, and data exploration to extract valuable insights. - Develop and optimize Machine Learning models to achieve high accuracy and performance. - Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness. - Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback. - Collaborate with cross-functional teams to align ML solutions with business goals. - Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models. - Bring in the latest advancements in ML and AI to drive innovation. Required Skills - 4-5 years of hands-on experience in Machine Learning and Deep Learning. - Strong expertise in feature engineering, data exploration, and data preprocessing. - Experience with imbalanced datasets and techniques to improve model generalization. - Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks. - Strong mathematical and statistical knowledge with problem-solving skills. - Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Skills - Experience with Big Data technologies (Hadoop, Spark, etc.) - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Experience in automating ML pipelines with MLOps practices. - Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Location: Bangalore/Hyderabad/Pune Experience level: 8+ Years About the Role We are looking for a technical and hands-on Lead Data Engineer to help drive the modernization of our data transformation workflows. We currently rely on legacy SQL scripts orchestrated via Airflow, and we are transitioning to a modular, scalable, CI/CD-driven DBT-based data platform. The ideal candidate has deep experience with DBT , modern data stack design , and has previously led similar migrations improving code quality, lineage visibility, performance, and engineering best practices. Key Responsibilities Lead the migration of legacy SQL-based ETL logic to DBT-based transformations Design and implement a scalable, modular DBT architecture (models, macros, packages) Audit and refactor legacy SQL for clarity, efficiency, and modularity Improve CI/CD pipelines for DBT: automated testing, deployment, and code quality enforcement Collaborate with data analysts, platform engineers, and business stakeholders to understand current gaps and define future data pipelines Own Airflow orchestration redesign where needed (e.g., DBT Cloud/API hooks or airflow-dbt integration) Define and enforce coding standards, review processes, and documentation practices Coach junior data engineers on DBT and SQL best practices Provide lineage and impact analysis improvements using DBTs built-in tools and metadata Must-Have Qualifications 8+ years of experience in data engineering Proven success in migrating legacy SQL to DBT , with visible results Deep understanding of DBT best practices , including model layering, Jinja templating, testing, and packages Proficient in SQL performance tuning , modular SQL design, and query optimization Experience with Airflow (Composer, MWAA), including DAG refactoring and task orchestration Hands-on experience with modern data stacks (e.g., Snowflake, BigQuery etc.) Familiarity with data testing and CI/CD for analytics workflows Strong communication and leadership skills; comfortable working cross-functionally Nice-to-Have Experience with DBT Cloud or DBT Core integrations with Airflow Familiarity with data governance and lineage tools (e.g., dbt docs, Alation) Exposure to Python (for custom Airflow operators/macros or utilities) Previous experience mentoring teams through modern data stack transitions

Role: Snowflake Developer with DBT Location: Bangalore/Hyderabad/Pune About the Role : We are seeking a Snowflake Developer with a deep understanding of DBT (data build tool) to help us design, build, and maintain scalable data pipelines. The ideal candidate will have hands-on experience working with Snowflake, DBT, and a passion for optimizing data processes for performance and efficiency. Responsibilities : Design, develop, and optimize Snowflake data models and DBT transformations. Build and maintain CI/CD pipelines for automated DBT workflows. Implement best practices for data pipeline performance, scalability, and efficiency in Snowflake. Contribute to the DBT community or develop internal tools/plugins to enhance the workflow. Troubleshoot and resolve complex data pipeline issues using DBT and Snowflake Qualifications : Must have minimum 4+ years of experience with Snowflake Must have at least 1 year of experience with DBT Extensive experience with DBT, including setting up CI/CD pipelines, optimizing performance, and contributing to the DBT community or plugins. Must be strong in SQL, data modelling, and ELT pipelines. Excellent problem-solving skills and the ability to collaborate effectively in a team environment.

Responsibilities - Conduct feature engineering, data analysis, and data exploration to extract valuable insights. - Develop and optimize Machine Learning models to achieve high accuracy and performance. - Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness. - Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback. - Collaborate with cross-functional teams to align ML solutions with business goals. - Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models. - Bring in the latest advancements in ML and AI to drive innovation. Required Skills - 4-5 years of hands-on experience in Machine Learning and Deep Learning. - Strong expertise in feature engineering, data exploration, and data preprocessing. - Experience with imbalanced datasets and techniques to improve model generalization. - Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks. - Strong mathematical and statistical knowledge with problem-solving skills. - Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Skills - Experience with Big Data technologies (Hadoop, Spark, etc.) - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Experience in automating ML pipelines with MLOps practices. - Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Key Responsibilities : - Conduct feature engineering, data analysis, and data exploration to extract valuable insights. - Develop and optimize Machine Learning models to achieve high accuracy and performance. - Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness. - Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback. - Collaborate with cross-functional teams to align ML solutions with business goals. - Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models. - Bring in the latest advancements in ML and AI to drive innovation. Requirements : - 4-5 years of hands-on experience in Machine Learning and Deep Learning. - Strong expertise in feature engineering, data exploration, and data preprocessing. - Experience with imbalanced datasets and techniques to improve model generalization. - Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks. - Strong mathematical and statistical knowledge with problem-solving skills. - Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Qualifications : - Experience with Big Data technologies (Hadoop, Spark, etc.) - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Experience in automating ML pipelines with MLOps practices. - Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Responsibilities - Conduct feature engineering, data analysis, and data exploration to extract valuable insights. - Develop and optimize Machine Learning models to achieve high accuracy and performance. - Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness. - Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback. - Collaborate with cross-functional teams to align ML solutions with business goals. - Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models. - Bring in the latest advancements in ML and AI to drive innovation. Required Skills - 4-5 years of hands-on experience in Machine Learning and Deep Learning. - Strong expertise in feature engineering, data exploration, and data preprocessing. - Experience with imbalanced datasets and techniques to improve model generalization. - Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks. - Strong mathematical and statistical knowledge with problem-solving skills. - Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Skills - Experience with Big Data technologies (Hadoop, Spark, etc.) - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Experience in automating ML pipelines with MLOps practices. - Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Key Responsibilities : - Conduct feature engineering, data analysis, and data exploration to extract valuable insights. - Develop and optimize Machine Learning models to achieve high accuracy and performance. - Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness. - Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback. - Collaborate with cross-functional teams to align ML solutions with business goals. - Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models. - Bring in the latest advancements in ML and AI to drive innovation. Requirements : - 4-5 years of hands-on experience in Machine Learning and Deep Learning. - Strong expertise in feature engineering, data exploration, and data preprocessing. - Experience with imbalanced datasets and techniques to improve model generalization. - Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks. - Strong mathematical and statistical knowledge with problem-solving skills. - Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Qualifications : - Experience with Big Data technologies (Hadoop, Spark, etc.) - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Experience in automating ML pipelines with MLOps practices. - Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Responsibilities: Design and implement scalable data pipelines to ingest, process, and analyze large volumes of structured and unstructured data from various sources. Develop and optimize data storage solutions, including data warehouses, data lakes, and NoSQL databases, to support efficient data retrieval and analysis. Implement data processing frameworks and tools such as Apache Hadoop, Spark, Kafka, and Flink to enable real-time and batch data processing. Collaborate with data scientists and analysts to understand data requirements and develop solutions that enable advanced analytics, machine learning, and reporting. Ensure data quality, integrity, and security by implementing best practices for data governance, metadata management, and data lineage. Monitor and troubleshoot data pipelines and infrastructure to ensure reliability, performance, and scalability. Develop and maintain ETL (Extract, Transform, Load) processes to integrate data from various sources and transform it into usable formats. Stay current with emerging technologies and trends in big data and cloud computing, and evaluate their applicability to enhance our data engineering capabilities. Document data architectures, pipelines, and processes to ensure clear communication and knowledge sharing across the team. Strong programming skills in Java, Python, or Scala.Strong understanding of data modelling, data warehousing, and ETL processes. Min 4 to Max 6yrs of Relevant exp.Strong understanding of Big Data technologies and their architectures, including Hadoop, Spark, and NoSQL databases. Locations : Mumbai, Delhi NCR, Bengaluru , Kolkata, Chennai, Hyderabad, Ahmedabad, Pune, Remote

Google Cloud Platform o GCS, DataProc, Big Query, Data Flow Programming Languages o Java, Scripting Languages like Python, Shell Script, SQL Google Cloud Platform o GCS, DataProc, Big Query, Data Flow 5+ years of experience in IT application delivery with proven experience in agile development methodologies 1 to 2 years of experience in Google Cloud Platform (GCS, DataProc, Big Query, Composer, Data Processing like Data Flow)

We are seeking a highly skilled Data Engineer with expertise in ETL techniques, programming, and big data technologies. The candidate will play a critical role in designing, developing, and maintaining robust data pipelines, ensuring data accuracy, consistency, and accessibility. This role involves collaboration with cross-functional teams to enrich and maintain a central data repository for advanced analytics and machine learning. The ideal candidate should have experience with cloud-based data platforms, data modeling, and data governance processes. Location - Bengaluru,Doddakannell, Karnataka, Sarjapur Road

Skill Big Data Spark SQL Scala Mandatory Certification any one Cloudera CCA Spark and Hadoop Developer CCA175 Databricks Certified Developer Apache Spark 2X Hortonworks HDP Certified Apache Spark Developer Job Description Experience in Scala programming languages Experience in Big Data technologies including Spark Scala and Kafka Who have a good understanding of organizational strategy architecture patterns Microservices Event Driven and technology choices and coaching the team in execution in alignment to these guidelines Who can apply organizational technology patterns effectively in projects and make recommendations on alternate options Who have handson experience working with large volumes of data including different patterns of data ingestion processing batch realtime movement storage and access for both internal and external to BU and ability to make independent decisions within scope of project Who have a good understanding of data structures and algorithms Who can test debug and fix issues within established SLAs Who can design software that is easily testable and observable Who understand how teams goals fit a business need Who can identify business problems at the project level and provide solutions Who understand data access patterns streaming technology data validation data performance cost optimization Strong SQL skills ETL TalenD preferred Any other ETL tool Experience with Linux OS user level Python or R programming skills good to have but not mandatory Mandatory Skills : Apache Spark,Scala,SparkSQL,Big Data Hadoop Ecosystem

Zalaris is looking for Senior Data Engineer to join our dynamic team and embark on a rewarding career journey Liaising with coworkers and clients to elucidate the requirements for each task. Conceptualizing and generating infrastructure that allows big data to be accessed and analyzed. Reformulating existing frameworks to optimize their functioning. Testing such structures to ensure that they are fit for use. Preparing raw data for manipulation by data scientists. Detecting and correcting errors in your work. Ensuring that your work remains backed up and readily accessible to relevant coworkers. Remaining up-to-date with industry standards and technological advancements that will improve the quality of your outputs.

Diverse Lynx is looking for Data Engineer to join our dynamic team and embark on a rewarding career journey Liaising with coworkers and clients to elucidate the requirements for each task. Conceptualizing and generating infrastructure that allows big data to be accessed and analyzed. Reformulating existing frameworks to optimize their functioning. Testing such structures to ensure that they are fit for use. Preparing raw data for manipulation by data scientists. Detecting and correcting errors in your work. Ensuring that your work remains backed up and readily accessible to relevant coworkers. Remaining up-to-date with industry standards and technological advancements that will improve the quality of your outputs.

JD : BigData and Hadoop JOB LOCATION : PUNE J D :: Experience in full stack software development with a focus on data-driven applications2. Experience with scale-out technologies like Hadoop, NiFi, Hive and Spark on-premise (With Java as a language)3. Strong understanding of database technologies, proficiency with SQL (Oracle preferably)4. Experience working with SpringBoot and Java technologies5. Background in big data with hands-on experience with Spark development6. Efficient knowledge of the internals of technologies in the Hadoop ecosystem is a plus.7. Proficiency with advanced object-oriented programming8. Excellent problem-solving and analytical skills9. Excellent written and oral communications skills10. Ability to mentor junior team members for Hadoop ecosystem