3 Graphdbs Jobs

Responsibilities: Create and optimize complex SPARQL Protocol and RDF Query Language queries to retrieve and analyze data from graph databases Develop graph-based applications and models to solve real-world problems and extract valuable insights from data. Design, develop, and maintain scalable data pipelines using Python rest apis get data from different cloud platforms Create and optimize complex SPARQL queries to retrieve and analyze data from graph databases. Study and understand the nodes, edges, and properties in graphs, to represent and store data in relational databases. Mandatory Skills- Python, RDF, Neo4J, GraphDB, Version Control System, API Frameworks Qualifications: Strong proficiency in SparQL, and RDF query language Strong proficiency in Python and Rest APIs Experience with database technologies sql and sparql Preferred Skills: Knowledge of cloud platforms like AWS, Azure, or GCP. Experience with version control systems like Github. Understanding of environments and deployment processes and cloud infrastructure.

The Data Scientist-3 in Bangalore (or Mumbai) will be part of the 811 Data Strategy Group that comprises Data Engineers, Data Scientists and Data Analytics professionals. He/she will be associated with one of the key functional areas such as Product Strategy, Cross Sell, Asset Risk, Fraud Risk, Customer Experience etc. and help build robust and scalable solutions that are deployed for real time or near real time consumption and integrated into our proprietary Customer Data Platform (CDP). This is an exciting opportunity to work on data driven analytical solutions and have a profound influence on the growth trajectory of a super fast evolving digital product. Key Requirements of The Role Advanced degree in an analytical field (e.g., Data Science, Computer Science, Engineering, Applied Mathematics, Statistics, Data Analysis) or substantial hands on work experience in the space 7 - 10 Years of relevant experience in the space Expertise in mining AI/ML opportunities from open ended business problems and drive solution design/development while closely collaborating with engineering, product and business teams Strong understanding of advanced data mining techniques, curating, processing and transforming data to produce sound datasets. Strong experience in NLP, time series forecasting and recommendation engines preferred Create great data stories with expertise in robust EDA and statistical inference. Should have at least a foundational understanding in Experimentation design ? Strong understanding of the Machine Learning lifecycle - feature engineering, training, validation, scaling, deployment, scoring, monitoring, and feedback loop. Exposure to Deep Learning applications and tools like TensorFlow, Theano, Torch, Caffe preferred Experience with analytical programming languages, tools and libraries (Python a must) as well as Shell scripting. Should be proficient in developing production ready code as per best practices. Experience in using Scala/Java/Go based libraries a big plus Very proficient is SQL and other relational databases along with PySpark or Spark SQL. Proficient is using NoSQL databases. Experience in using GraphDBs like Neo4j a plus. Candidate should be able to handle unstructured data with ease. Candidate should have experience in working with MLEs and be proficient (with experience) in using MLOps tools. Should be able to consume the capabilities of said tools with deep understanding of deployment lifecycle. Experience in CI/CD deployment is a big plus. Knowledge of key concepts in distributed systems like replication, serialization, concurrency control etc. a big plus Good understanding of programming best practices and building code artifacts for reuse. Should be comfortable with version controlling and collaborate comfortably in tools like git Ability to create frameworks that can perform model RCAs using analytical and interpretability tools. Should be able to peer review model documentations/code bases and find opportunities Experience in end-to-end delivery of AI driven Solutions (Deep learning , traditional data science projects) Strong communication, partnership and teamwork skills ? Should be able to guide and mentor teams while leading them by example. Should be an integral part of creating a team culture focused on driving collaboration, technical expertise and partnerships with other teams ? Ability to work in an extremely fast paced environment, meet deadlines, and perform at high standards with limited supervision A self-starter who is looking to build grounds up and contribute to the making of a potential big name in the space ? Experience in Banking and financial services is a plus. However, sound logical reasoning and first principles problem solving are even more critical job role: 1. As a key partner at the table, attend key meetings with the business team to bring in the data perspective to the discussions 2. Perform comprehensive data explorations around to generate inquisitive insights and scope out the problem 3. Develop simplistic to advanced solutions to address the problem at hand. We believe in making swift (albeit sometimes marginal) impact to business KPIs and hence adopt an MVP approach to solution development 4. Build re-usable code analytical frameworks to address commonly occurring business questions 5. Perform 360-degree customer profiling and opportunity analyses to guide new product strategy. This is a nascent business and hence opportunities to guide business strategy are plenty 6. Guide team members on data science and analytics best practices to help them overcome bottlenecks and challenges 7. The role will be an approximate 60% IC 40% leading and the ratios can vary basis need and fit 8. Develop Customer-360 Features that will be integrated into the Customer Data Platform (CDP) to enhance the single view of our customer

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.