Data Engineer

Project Role : Data Engineer Project Role Description : Design, develop and maintain data solutions for data generation, collection, and processing. Create data pipelines, ensure data quality, and implement ETL (extract, transform and load) processes to migrate and deploy data across systems. Must have skills : Data Engineering Good to have skills : NA Minimum 5 year(s) of experience is required Educational Qualification : 15 years full time education

Data Engineer – Azure Data Platform & Graph Experience: 5–8 years Location: India Role Summary A hands-on, build-and-run role: writing and operating the ETL/ELT pipelines, lakehouse tables, and graph data models that bring data from many enterprise source systems into a governed, analytics- and AI-ready platform. Day to day, this means writing transformation code, debugging failed pipeline runs, and modeling connected data directly in both property-graph (Cosmos DB Gremlin) and RDF/semantic-graph (SPARQL, Turtle) stores — not just designing on a whiteboard. Key Responsibilities Build, schedule, and maintain ETL/ELT pipelines — extracting from source systems, transforming with PySpark/Spark SQL, and loading into bronze/silver/gold lakehouse tables — using Fabric Data Factory pipelines and Dataflow Gen2 (or ADF/equivalent) Write and maintain transformation logic for incremental loads, change data capture (CDC), deduplication, slowly changing dimensions (SCD), and schema evolution Hands-on troubleshooting of failed or delayed pipeline runs — diagnosing root cause, fixing transformation bugs, and rebuilding/backfilling data as needed Model and query graph data (vertices, edges, properties) directly in Azure Cosmos DB for Apache Gremlin, writing and optimizing Gremlin traversals for relationship-heavy and knowledge-graph use cases Write SPARQL queries and author Turtle (.ttl) files to populate and query RDF-based knowledge graphs, working with a triplestore such as Graphwise GraphDB Build and publish data products aligned to data mesh principles — clear ownership, documented contracts, and discoverability for consuming teams Register, tag, and maintain lineage for data assets in an enterprise data catalog to support governed, self-service discovery Write data quality checks, schema validation rules, and pipeline monitoring/alerting across the ingestion-to-consumption flow Tune pipeline performance, partitioning strategy, and cost/throughput trade-offs across relational, NoSQL, and graph stores Collaborate with data architects, analytics engineers, and AI/ML teams to expose curated, trustworthy data for downstream consumption (BI, RAG/agentic AI, ML) Required Skills & Experience 5+ years hands-on building and operating ETL/ELT pipelines in production, across relational, NoSQL, and graph data stores Strong Python for pipeline and transformation development (PySpark, pandas, or equivalent) — comfortable writing and debugging transformation code daily Hands-on experience building pipelines on a modern Azure data platform (e.g., Microsoft Fabric, or Azure Synapse/Databricks-equivalent) — Data Factory/Dataflow Gen2, Spark notebooks, Delta Lake tables Practical experience implementing medallion (bronze/silver/gold) architecture, including incremental loads, CDC, deduplication, SCD, and schema evolution handling Hands-on with Azure Cosmos DB, including the Gremlin (graph) API — writing vertex/edge data models, partition key design, and Gremlin queries/traversals Working knowledge of RDF/semantic graph technologies — writing SPARQL queries and authoring Turtle (.ttl) files, using a triplestore such as Graphwise GraphDB (or equivalent, e.g., Amazon Neptune, Stardog) Strong SQL — writing and optimizing complex transformation and analytical queries Experience building integrations across multiple heterogeneous source systems (databases, SaaS applications, APIs, files) Working knowledge of data catalog / metadata management practices (lineage, classification, glossary) Understanding of data mesh concepts — data as a product, domain ownership, and self-serve data platforms Preferred Exposure to the broader semantic web stack — RDF/RDFS, OWL, SHACL, SKOS — for ontology-driven knowledge graph work Experience with pipeline orchestration tools (e.g., Apache Airflow, or Fabric's Airflow-based orchestration) Exposure to enterprise data mesh implementations, including federated governance models Familiarity with Microsoft Purview (or equivalent) for enterprise-wide metadata, data map, and unified catalog capabilities Experience with real-time/streaming ingestion (e.g., Eventstream, KQL, or equivalent) Exposure to data preparation for vector stores / RAG-style AI consumption Relevant platform certification (e.g., Microsoft Certified: Fabric Data Engineer Associate) Production experience across multiple major cloud platforms (AWS, Azure, and GCP) Education Bachelor's/Master's in Computer Science, Data Engineering, or related field

15 years full time education

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