Senior Manager of Software Engineering - Databricks, AWS

Job Description

This is your chance to change the path of your career and guide multiple teams to success at one of the world's leading financial institutions.

As a Manager of Software Engineering at JPMorgan

Chase within Corporate Sector, Enterprise Technology, you are an integral part of an agile team that works to enhance, build, and deliver trusted market‑leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives.

• Lead architecture and delivery of high‑throughput, low‑latency data pipelines using Databricks and Apache Spark (Core, SQL, Structured Streaming).
• Establish lakehouse patterns with Delta Lake (ACID transactions, schema evolution, time travel, Z‑ordering, compaction) and ensure performance at scale.
• Drive team adoption of enterprise‑authorized AI‑assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI‑assisted code review/refactoring, test strategy acceleration, incident/root‑cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
• Apply knowledge of tools within the Software Development Life Cycle toolchain, including enterprise‑authorized AI‑assisted development and automation capabilities, to improve the value realized by automation.
• Own Databricks cluster strategy and setup: runtime selection, autoscaling, driver/executor sizing, Spark configs, unit scripts, cluster policies, pools, and instance profiles.
• Orchestrate jobs with Databricks Workflows; integrate with AWS eventing and orchestration as needed.
• Design secure data ingestion and transformation frameworks leveraging Databricks services: design delta or unmanaged tables, create tasks for data ingestion process, create DAGs using Airflow to orchestrate creation of trusted and refined data.
• Enforce data quality, lineage, and governance using Unity Catalog and/or Glue Catalog; embed expectations and validation into pipelines.
• Drive Spark performance engineering: partitioning strategies, file sizing, AQE, broadcast joins, shuffle tuning, caching, spill/memory control, and job right‑sizing to optimize cost.
• Build reusable libraries, frameworks, and APIs in Python and/or Java; oversee unit, integration, and data validation testing.
• Implement CI/CD for data projects (Git‑based workflows), Terraform infrastructure deployments environment promotion, and automated deployments; champion engineering standards and code reviews.

• Formal training or certification on software engineering concepts and 5+ years applied experience.
• 10+ years of professional software/data engineering experience, including substantial production work with Spark on Databricks or EMR.
• Demonstrated experience leading effective use of approved AI‑assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices.
• Strong proficiency in Python and/or Java for data processing, platform tooling, and automation.
• Hands‑on Databricks expertise (Delta Lake, Unity Catalog, Workflows, Repos/notebooks, SQL Warehouses).
• Proven track record architecting and operating ETL/ELT pipelines (batch and streaming), with schema design/evolution, SLAs, and reliability engineering.
• Deep skills in Spark performance tuning and Databricks cluster setup/optimization.
• Strong SQL and analytics data modeling (dimensional/star schema; lakehouse best practices).
• CI/CD and automation tooling for data (Git…