About the role
As a Software Engineer III at JPMorgan Chase within the Asset and Wealth Management, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives.
Responsibilities;
- Designs, builds, and maintains batch and (as needed) streaming data pipelines using Databricks.
- Develops and optimises ETL/ELT workflows using PySpark / Spark SQL and Databricks workflows/jobs.
- Implements data modelling (bronze/silver/gold patterns), curation, and dataset publishing for analytics and consumption.
- Tune and optimise Spark jobs for performance, cost, and scalability (partitioning, file sizing, caching, joins, etc. ).
- Ensures strong data quality through validations, reconciliations, monitoring, and alerting.
- Works with stakeholders (data analysts, data scientists, product, and engineering teams) to translate requirements into data solutions.
- Implements and follows CI/CD and SDLC practices for data engineering code (testing, code reviews, version control).
- Supports production operations: incident triage, root-cause analysis, and pipeline reliability improvements.
- Contributes to documentation, standards, and reusable frameworks to improve team productivity.
Requirements:
- Formal training or certification on software engineering concepts and 3+ years of applied experience.
- Hands-on experience in Data Engineering.
- Strong experience with Databricks (jobs/workflows, notebooks, clusters, performance tuning).
- Proficiency in Python and SQL; strong hands-on experience in PySpark/Spark SQL.
- Experience in Data modeling, ETL/ELT, performance tuning, data quality, monitoring, and troubleshooting.
- Solid understanding of data pipeline architecture, orchestration concepts, and dependency management.
- Experience working with data lakes/lakehouse storage patterns and file formats (e. g., Parquet).
- Familiarity with Git-based workflows and engineering best practices.