Senior Lead Software Engineer - Data
On-site · Georgia, United States
Job Summary
Senior Lead Software Engineer - Data at JPMorgan Chase (Corporate Sector - Cloud Financial Management Technology) drives technical guidance, secure production code, and design decisions across multiple technologies. Responsibilities include mentoring technical teams, reviewing/debugging code, leading architecture evaluations with vendors and internal teams, advancing SDLC practices, advocating firmwide frameworks, improving operational stability, and fostering an inclusive team culture. Required skills include Python for data engineering, PySpark/SparkSQL, Spark performance tuning, S3 data lake design and security, experience with Iceberg/Delta/Hudi lakehouse patterns, and proficiency with distributed data warehouses (Redshift, BigQuery, Snowflake). Strong candidates will have hands-on experience delivering system design, application development, testing, and operations, plus deep knowledge of cloud, AI/ML, and agile methodologies; familiarity with FinTech IT environments is a plus.
Required Qualifications
- Formal training or certification on software engineering concepts or equivalent experience
- 5+ years applied experience in software engineering
- Proficiency in Python for data engineering
- Advanced PySpark/SparkSQL (DataFrame APIs, UDFs/pandas UDFs)
- Spark performance/tuning skills (partitioning, shuffle minimization, broadcast joins, AQE, skew handling, caching, backfills)
- S3 data lake design and security (bucket/partition layout, lifecycle/versioning, SSE-KMS encryption, cross-account access, Lake Formation/IAM)
- Experience with open table formats and lakehouse patterns (Iceberg, Delta Lake, Hudi)
- Experience with distributed columnar data warehouses (Redshift, BigQuery, Snowflake)
- Hands-on experience with system design, application development, testing, and operational stability
- Knowledge of CI/CD, Application Resiliency, and Security
- Understanding of financial services IT systems
- Ability to translate business requirements into data models and measurable outcomes
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