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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