- Design and build conformed dimensions and transactional fact tables for enterprise reporting.
- Develop and maintain data pipelines on A zure Databricks using PySpark, Spark SQL, and Delta Lake.
- Implement best practices in dimensional modeling, including:
- Star schemas
- Conformed dimensions
- Surrogate and deterministic hash keys (e.g., SHA-256)
- Transactional fact design
- Translate existing SQL Server and stored procedure logic into efficient Spark-based transformations.
- Implement incremental data processing and change data capture (CDC) pipelines.
- Ensure data reconciliation with source systems to meet accuracy and audit standards.
- Collaborate closely with a senior Databricks lead to deliver core data assets within defined timelines. Required
Skills:
- 8+ years of experience in data engineering, with strong focus on Azure Databricks .
- Proficiency in:
- PySpark
- Spark SQL
- Delta Lake
- Deep expertise in dimensional modelling and data warehousing concepts.
- Experience with:
- Building scalable production pipelines
- Incremental data loads and CDC patterns
- Performance optimization in distributed systems
- Robust understanding of relational databases and ability to interpret complex SQL/stored procedures Good to have Experience with:
- Lakeflow / Spark Declarative Pipelines or dbt
- Unity Catalog
- Exposure to financial/General Ledger data models
- Familiarity with JD Edwards systems, including:
- F0902 (balances)
- F0911 (journal details)
📌 Data Engineer (Bengaluru)
🏢 Datavail
📍 Bengaluru
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