Lead Software Engineer - Databricks, ML, AWS

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

As a Lead Software Engineer, Machine Learning and Cloud at JPMorgan Chase within the Corporate Technology
- Consumer & Community Bank Finance group, 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 and lead, 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.
• Drives 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.
• Applies 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.
• 8+ 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…