About the role
Azure Data Engineer with strong ADB (PySpark/Python), ADF; exposure to Snowflake; insurance domain basics; excellent communication; independent contributor; and effective use of GenAI tools (usage: Claude code/Claude skills/Genie) for productivity and quality.
Responsibilities:
- Ingest from varied sources (APIs, DBs, and flat files) into ADLS; design medallion/layered data models.
- Integrate with Snowflake (staging, modelling, performance tuning).
- Ensure data quality, observability, and reliability (DQ checks, lineage, and alerting).
Requirements:
- Basic to intermediate knowledge of Snowflake integrations.
- 4-9 years in data engineering; strong ADB (PySpark/Python) and ADF. Build and optimize scalable ETL/ELT pipelines in Azure Databricks (PySpark/Python). Hands-on with ADLS, Delta Lake, Spark performance tuning, partitioning, Z-Ordering, and caching.
- GenAI-assisted development: practical experience using prompts to generate/accelerate code, tests, SQL, and documentation; ability to evaluate and safely adapt AI outputs. (IMPORTANT).
- Orchestrate workflows using Azure Data Factory; manage CI/CD and environments, and secrets management (Key Vault).
- Strong hands-on SQL experience.
- Excellent communication and ability to work independently and with cross-functional teams. (IMPORTANT).
- Basic understanding of the insurance domain (policy, claims, underwriting, risk). (must-have).