237 Cloudera Jobs - Page 10

Who you areA Data Engineer specializing in enterprise data platforms, experienced in building, managing, and optimizing data pipelines for large-scale environments. Having expertise in big data technologies, distributed computing, data ingestion, and transformation frameworks. Proficient in Apache Spark, PySpark, Kafka, and Iceberg tables, and understand how to design and implement scalable, high-performance data processing solutions.What you’ll doAs a Data Engineer – Data Platform Services, responsibilities include: Data Ingestion & Processing Designing and developing data pipelines to migrate workloads from IIAS to Cloudera Data Lake. Implementing streaming and batch data ingestion frameworks using Kafka, Apache Spark (PySpark). Working with IBM CDC and Universal Data Mover to manage data replication and movement. Big Data & Data Lakehouse Management Implementing Apache Iceberg tables for efficient data storage and retrieval. Managing distributed data processing with Cloudera Data Platform (CDP). Ensuring data lineage, cataloging, and governance for compliance with Bank/regulatory policies. Optimization & Performance Tuning Optimizing Spark and PySpark jobs for performance and scalability. Implementing data partitioning, indexing, and caching to enhance query performance. Monitoring and troubleshooting pipeline failures and performance bottlenecks. Security & Compliance Ensuring secure data access, encryption, and masking using Thales CipherTrust. Implementing role-based access controls (RBAC) and data governance policies. Supporting metadata management and data quality initiatives. Collaboration & Automation Working closely with Data Scientists, Analysts, and DevOps teams to integrate data solutions. Automating data workflows using Airflow and implementing CI/CD pipelines with GitLab and Sonatype Nexus. Supporting Denodo-based data virtualization for seamless data access. Required education Bachelor's Degree Preferred education Master's Degree Required technical and professional expertise 4-7 years of experience in big data engineering, data integration, and distributed computing. Strong skills in Apache Spark, PySpark, Kafka, SQL, and Cloudera Data Platform (CDP). Proficiency in Python or Scala for data processing. Experience with data pipeline orchestration tools (Apache Airflow, Stonebranch UDM). Understanding of data security, encryption, and compliance frameworks. Preferred technical and professional experience Experience in banking or financial services data platforms. Exposure to Denodo for data virtualization and DGraph for graph-based insights. Familiarity with cloud data platforms (AWS, Azure, GCP). Certifications in Cloudera Data Engineering, IBM Data Engineering, or AWS Data Analytics..

Who you areA highly skilled Data Engineer specializing in Data Modeling with experience in designing, implementing, and optimizing data structures that support the storage, retrieval and processing of data for large-scale enterprise environments. Having expertise in conceptual, logical, and physical data modeling, along with a deep understanding of ETL processes, data lake architectures, and modern data platforms. Proficient in ERwin, PostgreSQL, Apache Iceberg, Cloudera Data Platform, and Denodo. Possess ability to work with cross-functional teams, data architects, and business stakeholders ensures that data models align with enterprise data strategies and support analytical use cases effectively. What you’ll doAs a Data Engineer – Data Modeling, you will be responsible for: Data Modeling & Architecture Designing and developing conceptual, logical, and physical data models to support data migration from IIAS to Cloudera Data Lake. Creating and optimizing data models for structured, semi-structured, and unstructured data stored in Apache Iceberg tables on Cloudera. Establishing data lineage and metadata management for the new data platform. Implementing Denodo-based data virtualization models to ensure seamless data access across multiple sources. Data Governance & Quality Ensuring data integrity, consistency, and compliance with regulatory standards, including Banking/regulatory guidelines. Implementing Talend Data Quality (DQ) solutions to maintain high data accuracy. Defining and enforcing naming conventions, data definitions, and business rules for structured and semi-structured data. ETL & Data Pipeline Optimization Supporting the migration of ETL workflows from IBM DataStage to PySpark, ensuring models align with the new ingestion framework. Collaborating with data engineers to define schema evolution strategies for Iceberg tables. Ensuring performance optimization for large-scale data processing on Cloudera. Collaboration & Documentation Working closely with business analysts, architects, and developers to translate business requirements into scalable data models. Documenting data dictionary, entity relationships, and mapping specifications for data migration. Supporting reporting and analytics teams (Qlik Sense/Tableau) by providing well-structured data models. Required education Bachelor's Degree Preferred education Master's Degree Required technical and professional expertise 4-7 years of experience in data modeling, database design, and data engineering. Hands-on experience with ERwin Data Modeler for creating and managing data models. Strong knowledge of relational databases (PostgreSQL) and big data platforms (Cloudera, Apache Iceberg). Proficiency in SQL and NoSQL database concepts. Understanding of data governance, metadata management, and data security principles. Familiarity with ETL processes and data pipeline optimization. Strong analytical, problem-solving, and documentation skills. Preferred technical and professional experience Experience working on Cloudera migration projects. Exposure to Denodo for data virtualization and Talend DQ for data quality management. Knowledge of Kafka, Airflow, and PySpark for data processing. Familiarity with GitLab, Sonatype Nexus, and CheckMarx for CI/CD and security compliance. Certifications in Data Modeling, Cloudera Data Engineering, or IBM Data Solutions.

Who you areA senior Data Scientist specializing in Advanced Analytics, with expertise in machine learning (ML), predictive modeling, and statistical analysis. Sound experience in leveraging Big-data technologies, AI, and automation to solve complex business problems and enhance decision-making. Have experience working with Cloudera Data Platform, Apache Spark, Kafka, and Iceberg tables, and you understand how to design and deploy scalable AI/ML models. Your role will be instrumental in data modernization efforts, applying AI-driven insights to enhance efficiency, optimize operations, and mitigate risks.What you’ll doAs a Data Scientist – Advanced Analytics, your responsibilities include: AI & Machine Learning Model Development Developing AI/ML models for predictive analytics, fraud detection, and customer segmentation. Implementing time-series forecasting, anomaly detection, and optimization models. Working with deep learning (DL) and Natural Language Processing (NLP) for AI-driven automation. Big Data & Scalable AI Pipelines Processing and analyzing large datasets using Apache Spark, PySpark, and Iceberg tables. Deploying real-time models and streaming analytics with Kafka. Supporting AI model training and deployment on Cloudera Machine Learning (CML). Advanced Analytics & Business Impact Performing exploratory data analysis (EDA) and statistical modelling. Delivering AI-driven insights to improve business decision-making. Supporting data quality and governance initiatives using Talend DQ. Data Governance & Security Ensuring AI models comply with Bank’s data governance and security policies. Implementing AI-driven anomaly detection and metadata management. Utilizing Thales CipherTrust for data encryption and compliance. Collaboration & Thought Leadership Working closely with data engineers, analysts, and business teams to integrate AI-driven solutions. Presenting AI insights and recommendations to stakeholders and leadership teams. Contributing to the development of best practices for AI and analytics. Required education Bachelor's Degree Preferred education Master's Degree Required technical and professional expertise 3-7 years of experience in AI, ML, and Advanced Analytics. Proficiency in Python, R, SQL, and ML frameworks (Scikit-learn, TensorFlow, PyTorch). Hands-on experience with Big-data technologies (Cloudera, Apache Spark, Kafka, Iceberg table format). Strong knowledge of statistical modelling, optimization, and feature engineering. Understanding of MLOps practices and AI model deployment. Preferred technical and professional experience Develop and implement advanced analytics models, including predictive, prescriptive, and diagnostic analytics to solve business challenges and optimize decision-making processes. Utilize tools and technologies to work with Large and complex datasets to derive analytical solutions. Build and deploy machine learning models (supervised and unsupervised), statistical models, and data-driven algorithms for forecasting, segmentation, classification, and anomaly detection. Should have strong hands-on experience in Python, Spark and cloud computing. Should be independently working and be able to deploy deep learning models using various architectures. Should be able to perform exploratory data analysis (EDA) to uncover trends, relationships, and outliers in large, complex datasets. Design and create features that improve model accuracy and business relevance. Should create insightful visualizations and dashboards that communicate findings to stakeholders. Effectively translate complex data insights into clear and actionable recommendations. Work closely with business leaders, engineers, and analysts to understand business requirements and translate them into analytical solutions that address strategic goals. Exposure to Graph AI using DGraph Enterprise. Knowledge of cloud-based AI platforms (AWS SageMaker, Azure ML, GCP Vertex AI).

A Data Engineer specializing in enterprise data platforms, experienced in building, managing, and optimizing data pipelines for large-scale environments. Having expertise in big data technologies, distributed computing, data ingestion, and transformation frameworks. Proficient in Apache Spark, PySpark, Kafka, and Iceberg tables, and understand how to design and implement scalable, high-performance data processing solutions.What you’ll doAs a Data Engineer – Data Platform Services, responsibilities include: Data Ingestion & Processing Designing and developing data pipelines to migrate workloads from IIAS to Cloudera Data Lake. Implementing streaming and batch data ingestion frameworks using Kafka, Apache Spark (PySpark). Working with IBM CDC and Universal Data Mover to manage data replication and movement. Big Data & Data Lakehouse Management Implementing Apache Iceberg tables for efficient data storage and retrieval. Managing distributed data processing with Cloudera Data Platform (CDP). Ensuring data lineage, cataloging, and governance for compliance with Bank/regulatory policies. Optimization & Performance Tuning Optimizing Spark and PySpark jobs for performance and scalability. Implementing data partitioning, indexing, and caching to enhance query performance. Monitoring and troubleshooting pipeline failures and performance bottlenecks. Security & Compliance Ensuring secure data access, encryption, and masking using Thales CipherTrust. Implementing role-based access controls (RBAC) and data governance policies. Supporting metadata management and data quality initiatives. Collaboration & Automation Working closely with Data Scientists, Analysts, and DevOps teams to integrate data solutions. Automating data workflows using Airflow and implementing CI/CD pipelines with GitLab and Sonatype Nexus. Supporting Denodo-based data virtualization for seamless data access. Required education Bachelor's Degree Preferred education Master's Degree Required technical and professional expertise 6-10 years of experience in big data engineering, data processing, and distributed computing. Proficiency in Apache Spark, PySpark, Kafka, Iceberg, and Cloudera Data Platform (CDP). Strong programming skills in Python, Scala, and SQL. Experience with data pipeline orchestration tools (Apache Airflow, Stonebranch UDM). Knowledge of data security, encryption, and compliance frameworks. Experience working with metadata management and data quality solutions. Preferred technical and professional experience Experience with data migration projects in the banking/financial sector. Knowledge of graph databases (DGraph Enterprise) and data virtualization (Denodo). Exposure to cloud-based data platforms (AWS, Azure, GCP). Familiarity with MLOps integration for AI-driven data processing. Certifications in Cloudera Data Engineering, IBM Data Engineering, or AWS Data Analytics. Architectural review and recommendations on the migration/transformation solutions. Experience working with Banking Data model. “Meghdoot” Cloud platform knowledge.

Who you areA highly skilled Data Engineer specializing in Data Modeling with experience in designing, implementing, and optimizing data structures that support the storage, retrieval and processing of data for large-scale enterprise environments. Having expertise in conceptual, logical, and physical data modeling, along with a deep understanding of ETL processes, data lake architectures, and modern data platforms. Proficient in ERwin, PostgreSQL, Apache Iceberg, Cloudera Data Platform, and Denodo. Possess ability to work with cross-functional teams, data architects, and business stakeholders ensures that data models align with enterprise data strategies and support analytical use cases effectively. What you’ll doAs a Data Engineer – Data Modeling, you will be responsible for: Data Modeling & Architecture Designing and developing conceptual, logical, and physical data models to support data migration from IIAS to Cloudera Data Lake. Creating and optimizing data models for structured, semi-structured, and unstructured data stored in Apache Iceberg tables on Cloudera. Establishing data lineage and metadata management for the new data platform. Implementing Denodo-based data virtualization models to ensure seamless data access across multiple sources. Data Governance & Quality Ensuring data integrity, consistency, and compliance with regulatory standards, including Banking/regulatory guidelines. Implementing Talend Data Quality (DQ) solutions to maintain high data accuracy. Defining and enforcing naming conventions, data definitions, and business rules for structured and semi-structured data. ETL & Data Pipeline Optimization Supporting the migration of ETL workflows from IBM DataStage to PySpark, ensuring models align with the new ingestion framework. Collaborating with data engineers to define schema evolution strategies for Iceberg tables. Ensuring performance optimization for large-scale data processing on Cloudera. Collaboration & Documentation Working closely with business analysts, architects, and developers to translate business requirements into scalable data models. Documenting data dictionary, entity relationships, and mapping specifications for data migration. Supporting reporting and analytics teams (Qlik Sense/Tableau) by providing well-structured data models. Required education Bachelor's Degree Preferred education Master's Degree Required technical and professional expertise Experience in Cloudera migration projects in the banking or financial sector. Knowledge of PySpark, Kafka, Airflow, and cloud-native data processing. Experience with Talend DQ for data quality monitoring. Preferred technical and professional experience Experience in Cloudera migration projects in the banking or financial sector. Knowledge of PySpark, Kafka, Airflow, and cloud-native data processing. Experience with Talend DQ for data quality monitoring. Familiarity with graph databases (DGraph Enterprise) for data relationships. Experience with GitLab, Sonatype Nexus, and CheckMarx for CI/CD and security compliance. IBM, Cloudera, or AWS/GCP certifications in Data Engineering or Data Modeling.

As Data Engineer, you will develop, maintain, evaluate and test big data solutions. You will be involved in the development of data solutions using Spark Framework with Python or Scala on Hadoop and AWS Cloud Data Platform Experienced in building data pipelines to Ingest, process, and transform data from files, streams and databases. Process the data with Spark, Python, PySpark, Scala, and Hive, Hbase or other NoSQL databases on Cloud Data Platforms (AWS) or HDFS Experienced in develop efficient software code for multiple use cases leveraging Spark Framework / using Python or Scala and Big Data technologies for various use cases built on the platform Experience in developing streaming pipelines Experience to work with Hadoop / AWS eco system components to implement scalable solutions to meet the ever-increasing data volumes, using big data/cloud technologies Apache Spark, Kafka, any Cloud computing etc Required education Bachelor's Degree Preferred education Master's Degree Required technical and professional expertise Total 5 - 7+ years of experience in Data Management (DW, DL, Data Platform, Lakehouse) and Data Engineering skills Minimum 4+ years of experience in Big Data technologies with extensive data engineering experience in Spark / Python or Scala. Minimum 3 years of experience on Cloud Data Platforms on AWS; Exposure to streaming solutions and message brokers like Kafka technologies. Experience in AWS EMR / AWS Glue / DataBricks, AWS RedShift, DynamoDB Good to excellent SQL skills Preferred technical and professional experience Certification in AWS and Data Bricks or Cloudera Spark Certified developers AWS S3 , Redshift , and EMR for data storage and distributed processing. AWS Lambda , AWS Step Functions , and AWS Glue to build serverless, event-driven data workflows and orchestrate ETL processes

What you’ll doAs a Data Engineer – Data Platform Services, you will be responsible for: Data Migration & Modernization Leading the migration of ETL workflows from IBM DataStage to PySpark, ensuring performance optimization and cost efficiency. Designing and implementing data ingestion frameworks using Kafka and PySpark, replacing legacy ETL Pipeline using DataStage. Migrating the analytical platform from IBM Integrated Analytics System (IIAS) to Cloudera Data Lake on CDP. Data Engineering & Pipeline Development Developing and maintaining scalable, fault-tolerant, and optimized data pipelines on Cloudera Data Platform. Implementing data transformations, enrichment, and quality checks to ensure accuracy and reliability. Leveraging Denodo for data virtualization and enabling seamless access to distributed datasets. Performance Tuning & Optimization Optimizing PySpark jobs for efficiency, scalability, and reduced cost on Cloudera. Fine-tuning query performance on Iceberg tables and ensuring efficient data storage and retrieval. Collaborating with Cloudera ML engineers to integrate machine learning workloads into data pipelines. Security & Compliance Implementing Thales CipherTrust encryption and tokenization mechanisms for secure data processing. Ensuring compliance with Bank/regulatory body security guidelines, data governance policies, and best practices. Collaboration & Leadership Working closely with business stakeholders, architects, and data scientists to align solutions with business goals. Leading and mentoring junior data engineers, conducting code reviews, and promoting best practices. Collaborating with DevOps teams to streamline CI/CD pipelines, using GitLab and Nexus Repository for efficient deployments. Required education Bachelor's Degree Preferred education Master's Degree Required technical and professional expertise 12+ years of experience in Data Engineering, ETL, and Data Platform Modernization. Hands-on experience in IBM DataStage and PySpark, with a track record of migrating legacy ETL workloads. Expertise in Apache Iceberg, Cloudera Data Platform, and Big-data processing frameworks. Strong knowledge of Kafka, Airflow, and cloud-native data processing solutions. Experience with Denodo for data virtualization and Talend DQ for data quality. Proficiency in SQL, NoSQL, and Graph DBs (DGraph Enterprise). Strong understanding of data security, encryption, and compliance standards (Thales CipherTrust). Experience with DevOps, CI/CD pipelines, GitLab, and Sonatype Nexus Repository. Excellent problem-solving, analytical, and communication skills. Preferred technical and professional experience Experience with Cloudera migration projects in Banking or financial domains. Experience working with Banking Data model. Knowledge of Cloudera ML, Qlik Sense/Tableau reporting, and integration with data lakes. Hands-on experience with QuerySurge for automated data testing. Understanding of code quality and security best practices using CheckMarx. IBM, Cloudera, or AWS/GCP certifications in Data Engineering, Cloud, or Security. “Meghdoot” Cloud platform knowledge. Architectural designing and recommendations the best possible solutions.

Who you areA senior Data Scientist specializing in Advanced Analytics, with expertise in machine learning (ML), predictive modeling, and statistical analysis. Sound experience in leveraging Big-data technologies, AI, and automation to solve complex business problems and enhance decision-making. Have experience working with Cloudera Data Platform, Apache Spark, Kafka, and Iceberg tables, and you understand how to design and deploy scalable AI/ML models. Your role will be instrumental in data modernization efforts, applying AI-driven insights to enhance efficiency, optimize operations, and mitigate risks.What you’ll doAs a Data Scientist – Advanced Analytics, your responsibilities include: AI & Machine Learning Model Development Developing AI/ML models for predictive analytics, fraud detection, and customer segmentation. Implementing time-series forecasting, anomaly detection, and optimization models. Working with deep learning (DL) and Natural Language Processing (NLP) for AI-driven automation. Big Data & Scalable AI Pipelines Processing and analyzing large datasets using Apache Spark, PySpark, and Iceberg tables. Deploying real-time models and streaming analytics with Kafka. Supporting AI model training and deployment on Cloudera Machine Learning (CML). Advanced Analytics & Business Impact Performing exploratory data analysis (EDA) and statistical modelling. Delivering AI-driven insights to improve business decision-making. Supporting data quality and governance initiatives using Talend DQ. Data Governance & Security Ensuring AI models comply with Bank’s data governance and security policies. Implementing AI-driven anomaly detection and metadata management. Utilizing Thales CipherTrust for data encryption and compliance. Collaboration & Thought Leadership Working closely with data engineers, analysts, and business teams to integrate AI-driven solutions. Presenting AI insights and recommendations to stakeholders and leadership teams. Contributing to the development of best practices for AI and analytics. Required education Bachelor's Degree Preferred education Master's Degree Required technical and professional expertise 6-10 years of experience in AI, ML, and Advanced Analytics. Strong programming skills in Python, R, and SQL. Expertise in ML frameworks (TensorFlow, PyTorch, Scikit-learn). Experience with big data platforms (Cloudera, Apache Spark, Kafka, Iceberg). Strong background in statistical modelling, optimization, and time-series forecasting. Experience with MLOps and model deployment on cloud platforms. Preferred technical and professional experience Should have over 10 Yrs of experience in developing and maintaining the advanced analytics as a data scientist. Develop and implement advanced analytics models, including predictive, prescriptive, and diagnostic analytics to solve business challenges and optimize decision-making processes. Utilize tools and technologies to work with Large and complex datasets to derive analytical solutions. Build and deploy machine learning models (supervised and unsupervised), statistical models, and data-driven algorithms for forecasting, segmentation, classification, and anomaly detection. Should have strong hands-on experience in Python, Spark and cloud computing. Should be independently working and be able to deploy deep learning models using various architectures. Should be able to perform exploratory data analysis (EDA) to uncover trends, relationships, and outliers in large, complex datasets. Design and create features that improve model accuracy and business relevance. Should create insightful visualizations and dashboards that communicate findings to stakeholders. Effectively translate complex data insights into clear and actionable recommendations. To design, review and recommend the ML algorithms and provide a suitable solution for the business need. Work closely with business leaders, engineers, and analysts to understand business requirements and translate them into analytical solutions that address strategic goals. Experience in working with Banking Data model by implementing any analytical solution. Exposure to Graph AI using DGraph.

Data Engineer Summary Apply Now Full-Time 4+ years Responsibilities Design, develop, and maintain data pipelines and ETL processes. Build and optimize data architectures for analytics and reporting. Collaborate with data scientists and analysts to support data-driven initiatives. Implement data security and governance best practices. Monitor and troubleshoot data infrastructure and ensure high availability. Qualifications Design, develop, and maintain data pipelines and ETL processes. Build and optimize data architectures for analytics and reporting. Collaborate with data scientists and analysts to support data-driven initiatives. Implement data security and governance best practices. Monitor and troubleshoot data infrastructure and ensure high availability. Skills Proficiency in data engineering tools (Hadoop, Spark, Kafka, etc.). Strong SQL and programming skills (Python, Java, etc.). Experience with cloud platforms (AWS, Azure, GCP). Knowledge of data modeling, warehousing, and ETL processes. Strong problem-solving and analytical abilities.

6+ years of total IT experience 3+ years of experience with Hadoop (Cloudera)/big data technologies Knowledge of the Hadoop ecosystem and Big Data technologies Hands-on experience with the Hadoop eco-system (HDFS, MapReduce, Hive, Pig, Impala, Spark, Kafka, Kudu, Solr) Experience in designing and developing Data Pipelines for Data Ingestion or Transformation using Java Scala or Python. Experience with Spark programming (Pyspark, Scala, or Java) Hands-on experience with Python/Pyspark/Scala and basic libraries for machine learning is required. Proficient in programming in Java or Python with prior Apache Beam/Spark experience a plus. Hand on experience in CI/CD, Scheduling and Scripting Ensure automation through CI/CD across platforms both in cloud and on-premises System level understanding - Data structures, algorithms, distributed storage & compute Can-do attitude on solving complex business problems, good interpersonal and teamwork skills

As Data Engineer, you will develop, maintain, evaluate and test big data solutions. You will be involved in the development of data solutions using Spark Framework with Python or Scala on Hadoop and Azure Cloud Data Platform Responsibilities: Experienced in building data pipelines to Ingest, process, and transform data from files, streams and databases. Process the data with Spark, Python, PySpark and Hive, Hbase or other NoSQL databases on Azure Cloud Data Platform or HDFS Experienced in develop efficient software code for multiple use cases leveraging Spark Framework / using Python or Scala and Big Data technologies for various use cases built on the platform Experience in developing streaming pipelines Experience to work with Hadoop / Azure eco system components to implement scalable solutions to meet the ever-increasing data volumes, using big data/cloud technologies Apache Spark, Kafka, any Cloud computing etc Required education Bachelor's Degree Preferred education Master's Degree Required technical and professional expertise Minimum 4+ years of experience in Big Data technologies with extensive data engineering experience in Spark / Python or Scala; Minimum 3 years of experience on Cloud Data Platforms on Azure; Experience in DataBricks / Azure HDInsight / Azure Data Factory, Synapse, SQL Server DB Good to excellent SQL skills Exposure to streaming solutions and message brokers like Kafka technologies Preferred technical and professional experience Certification in Azure and Data Bricks or Cloudera Spark Certified developers

As a Big Data Engineer, you will develop, maintain, evaluate, and test big data solutions. You will be involved in data engineering activities like creating pipelines/workflows for Source to Target and implementing solutions that tackle the clients needs. Your primary responsibilities include: Design, build, optimize and support new and existing data models and ETL processes based on our clients business requirements. Build, deploy and manage data infrastructure that can adequately handle the needs of a rapidly growing data driven organization. Coordinate data access and security to enable data scientists and analysts to easily access to data whenever they need too. Required education Bachelor's Degree Preferred education Master's Degree Required technical and professional expertise Must have 5+ years exp in Big Data -Hadoop Spark -Scala ,Python Hbase, Hive Good to have Aws -S3, athena ,Dynomo DB, Lambda, Jenkins GIT Developed Python and pyspark programs for data analysis. Good working experience with python to develop Custom Framework for generating of rules (just like rules engine). Developed Python code to gather the data from HBase and designs the solution to implement using Pyspark. Apache Spark DataFrames/RDD's were used to apply business transformations and utilized Hive Context objects to perform read/write operations.. Preferred technical and professional experience Understanding of Devops. Experience in building scalable end-to-end data ingestion and processing solutions Experience with object-oriented and/or functional programming languages, such as Python, Java and Scala"