Role: Senior Data Engineer

Years of Experience: 5-8 Years

Location: Ahmedabad, Pune, Indore

Notice Period: Immediate to 30 Days Max

Must Skill: Python, SQL, Azure Databricks, ADF, ETL, Pyspark,

Role Summary

Build and operate scalable, reliable data pipelines on Azure. Develop batch and streaming ingestion, transform data using Databricks (PySpark/SQL), enforce data quality, and publish curated datasets for analytics and ML.

Key Responsibilities

- Design, build, and maintain ETL/ELT pipelines in Azure Data Factory and Databricks across Bronze Silver Gold layers.
- Implement Delta Lake best practices (ACID, schema evolution, MERGE/upsert, time travel, Z-ORDER).
- Write performant PySpark and SQL; tune jobs (partitioning, caching, join strategies).
- Create reusable components; manage code in Git; contribute to CI/CD pipelines (Azure DevOps/GitHub Actions/Jenkins).
- Apply data quality checks (Great Expectations or custom validations), monitoring, drift detection, and alerting.
- Model data for analytics (star/dimensional); publish to Synapse/Snowflake/SQL Server.
- Uphold governance and security (Purview/Unity Catalog lineage, RBAC, tagging, encryption, PII handling).
- Author documentation/runbooks; support production incidents and root-cause analysis; suggest cost/performance improvements.

Must-Have (Mandatory)

- Data Engineering & Pipelines
- Hands-on experience building production pipelines with Azure Data Factory and Databricks (PySpark/SQL).
- Working knowledge of Medallion Architecture and Delta Lake (schema evolution, ACID).
- Programming & Automation
- Strong Python (pandas/PySpark) and SQL.




- Practical Git workflow; experience integrating pipelines into CI/CD (Azure DevOps/GitHub Actions/Jenkins).
- Familiarity with packaging reusable code (e.g., Python wheels) and configuration-driven jobs.
- Data Modeling & Warehousing
- Solid grasp of dimensional modeling/star schemas; experience with Synapse, Snowflake, or SQL Server.
- Data Quality & Monitoring
- Implemented validation checks and alerts; exposure to drift detection and pipeline observability.
- Cloud Platforms (Azure preferred)
- ADLS Gen2, Key Vault, Databricks, ADF basics (linked services, datasets, triggers), environment promotion.
- Data Governance & Security
- Experience with metadata/lineage (Purview/Unity Catalog), RBAC, secrets management, and secure data sharing.
- Understanding of PII/PHI handling and encryption at rest/in transit.
- Collaboration
- Clear communication, documentation discipline, Agile ways of working, and code reviews.

Positive-to-Have

- Databricks Asset Bundles (DAB) for environment promotion/infra-as-code style deployments.
- Streaming/real-time: Kafka/Event Hubs; CDC tools (e.g., Debezium, ADF/Synapse CDC).
- MLOps touchpoints: MLflow tracking/registry, feature tables, basic model-inference pipelines.
- Power BI exposure for publishing curated tables and building operational KPIs.
- DataOps practices: automated testing, data contracts, lineage-aware deployments, cost optimization on Azure.
- Certifications: Microsoft Certified Azure Data Engineer Associate (DP-203) or equivalent.

Qualifications

- 46 years of professional experience in data engineering (or equivalent project depth).
- Bachelor’s/Master’s in CS/IT/Engineering or related field (or equivalent practical experience).

📌 Azure Data Engineer (Indore)
🏢 Einfochips
📍 Indore