Data Engineer III - Databricks, Pyspark, Python, AWS
JPMorgan Chase & Co. (company site, opens in a new tab)3+ yrsBengaluruOn-siteDirect career pageEst. 14–28 LPA
Posted on 22 Sept
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Job description
Structured overview of role & requirements
About This Role
- Design, develop, and maintain big data pipelines (batch and streaming) using PySpark/Spark and Databricks, including building scalable ingestion and transformation workflows for high-volume datasets with reliability, quality, and performance.
- Lead data modelling and solution design for data products, defining target architectures, data flows, and transformation patterns to support analytics-ready data layers.
- Optimize complex SQL transformations and perform advanced debugging/troubleshooting of distributed Spark workloads, applying modular design, efficient coding, and CI-friendly development with version control workflows.
Minimum Requirements
- 3+ years of applied experience in data engineering / big data engineering with formal training or certification in data engineering concepts.
- Expert-level SQL skills including complex joins, window functions, CTEs, query optimization at scale.
- Strong hands-on programming experience with Python, Apache Spark/PySpark (including performance tuning), and Databricks for large-scale data processing.
- Familiarity with AWS S3 and common AWS data processing services; experience with batch and streaming data processing solutions.
Ideal Candidate Profile
- Experienced in building scalable, maintainable data platforms with strong data warehousing knowledge, including dimensional modeling and SCDs.
- Capable of leading the end-to-end design and delivery of robust big data solutions working with cross-functional teams, emphasizing performance and quality.
- Comfortable using enterprise-authorized AI tools for data engineering workflows with strong validation and security awareness.