Software Engineer III Data Engineer, Databricks

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.

As a Software Engineer III at JPMorgan Chase within the Asset & 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 firms business objectives.

Job responsibilities

• Designs build and maintainbatch and (as needed) streaming data pipelinesusingDatabricks.
• Develops and optimizeETL/ELT workflowsusingPySpark / Spark SQLand Databricks workflows/jobs.
• Implementsdata modeling(bronze/silver/gold patterns) curation and dataset publishing for analytics and consumption.
• Tunes and optimize Spark jobs forperformance cost and scalability(partitioning file sizing caching joins etc.).
• Ensures strongdata qualitythrough validations reconciliations monitoring and alerting.
• Works with stakeholders (data analysts data scientists product and engineering teams) to translate requirements into data solutions.
• Implements and followCI/CD and SDLC practicesfor 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.

Required qualifications capabilities and skills

• Formal training or certification onsoftware engineeringconcepts and 3 years applied experience
• Hands-on experience inData Engineering.
• Strong experience withDatabricks(jobs/workflows notebooks clusters performance tuning).
• Proficiency inPythonandSQL; strong hands-on inPySpark/Spark SQL.
• Experience in Data modeling ETL/ELT performance tuning data quality monitoring troubleshooting.
• Solid understanding ofdata pipeline architecture orchestration concepts and dependency management.
• Experience working withdata lakes/lakehousestorage patterns and file formats (e.g. Parquet).
• Familiarity withGit-basedworkflows and engineering best practices.

Preferred qualifications capabilities and skills

• AI/ML exposure as an added advantage: experience supporting ML workflows by building feature datasets training/serving data pipelines or enabling model monitoring and experimentation (e.g. working with data scientists on reproducible data inputs feature engineering and ML-ready tables).

• Familiarity with ML ecosystem/tools is a plus (examples:MLflow Databricks model registry notebooks-based experimentation) and understanding of basic ML concepts (training vs inference leakage drift).

  Experience withDelta Lakefeatures (ACID tables time travel optimization).

• Exposure tostreaming(e.g. Spark Structured Streaming) and event-driven patterns.

  Experience with cloud platforms (AWS/Azure/GCP) and cloud storage integrations.

• Knowledge ofdata governance access controls and secure handling of sensitive data.

  Familiarity with orchestration tools (e.g. Airflow or similar) and supportingproduction-gradedata platforms (monitoring SLAs on-call rotations).