Azure Data Engineer - Artefact

Ovii's Interpretation of the Role

We need a seasoned Azure Data Engineer to design, build, and operate Python‑driven data pipelines and medallion‑style lakehouse structures. The role is hands‑on, requiring strong Python and SQL expertise, ETL/ELT design at scale, and familiarity with cloud data platforms.

Role Snapshot

• Build and maintain Python data pipelines
• Design ETL/ELT processes using Medallion architecture
• Develop SQL transformations and stored procedures
• Implement data quality checks and monitoring
• Support CI/CD for data workloads
• Document pipelines, data models, and runbooks
• Collaborate with Data & AI team

Must-Have Requirements

• Python development
• SQL querying and transformation
• ETL/ELT design at scale
• Medallion architecture knowledge
• Data orchestration tool experience
• Cloud data platform familiarity (Azure, AWS, GCP)
• Python development for data pipelines
• SQL data transformations
• Medallion lakehouse architecture

Nice-to-Have Signals

• Data quality tooling (Informatica IDQ, Microsoft Purview, Great Expectations)
• Data cataloguing (Informatica EDC)
• Data governance platforms (Informatica Axon, Microsoft Purview)
• Power BI or similar BI tools
• Familiarity with relational and non‑relational databases
• Data quality tooling
• Data governance and cataloguing
• BI reporting (Power BI)

Work Setup

• Location: Gurugram, India
• Work mode: ONSITE
• Employment type: Full-Time

Not Specified in JD

• Visa sponsorship
• Salary range
• Remote eligibility
• Education requirement
• Certifications
• Relocation
• Notice period
• Travel
• Security clearance
• Coding test
• Portfolio
• GitHub
• Writing sample
• Cover letter

What You'll Likely Work On

• Design, code, and deploy end‑to‑end Python pipelines that ingest, transform, and deliver data across bronze, silver, and gold layers.
• Create and optimise ETL/ELT workflows, including SQL‑based transformations, stored procedures, and views.
• Define data models, schemas, and storage strategies to support downstream analytics.
• Implement data‑quality rules (completeness, validity, consistency, uniqueness, accuracy, timeliness) within pipelines.
• Monitor pipeline health, set up alerts, and troubleshoot production issues.
• Contribute to CI/CD pipelines for data workloads, including automated testing and version control.
• Produce clear technical documentation, runbooks, and knowledge‑base articles.

Good Fit If You Have

• Enjoys debugging complex data pipeline failures.
• Has exposure to data governance or cataloguing tools.
• Comfortable writing production‑grade Python code and documentation.
• Familiar with BI reporting tools such as Power BI.