Data Engineer

Data Engineer

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.

Data Engineering

NA

Minimum

year(s) of experience is required

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

About Accenture

Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities.

Visit us at www.accenture.com 

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