Job Title : Azure Databricks Engineer

Location : Bengaluru (Hybrid)

Experience : 5 to 8 Years

Role Description :

- Azure Databricks engineering notebooks, Jobs/Workflows, Delta tables, Unity Catalog; strong Spark SQL; tidy structure for fast experiments.

- Working with complex business data (not big data) many dependencies between tables, reference lists, hierarchies, many-to-many links, and tricky business rules.

- Single wide table design (analytics + ML ready) builds one clean, well-defined gold table (one row per entity, e.g., invoice, PO, worker-week) with consistent keys, clear feature columns, and minimal joins.

- ETL/ELT orchestration & reliability scheduling, retries, safe re-runs (same result each time), backfills, data lineage, and clear run logs/monitoring.

- Data quality checks automated validation rules, anomaly detection, reconciliation, deduplication, and trustworthy KPI mindset.

- Python for data (PySpark + APIs) reusable modules, calling external APIs, parsing XML/JSON, writing basic tests.

- SAP knowledge (VIM / P2P / FI) invoice lifecycle, PO/invoice concepts, status/history logs, and common SAP/VIM issues.

- PoC speed mindset quick iterations, clear assumptions, fast refactors; performance tuning only if it blocks progress.

- Maintainable PoC craftsmanship even when moving fast : consistent naming, modular queries/functions, parameterisation, and minimal-but-solid documentation so the PoC can be moved to production later if it proves value.

- Copilot used smartly (with control) uses Copilot to write code faster, but always reviews logic and adds checks/tests (no blind copy-paste).