Location: Wilmington, DE, United States

Be an integral part of an agile team thats constantly pushing the envelope to enhance, build, and deliver top-notch technology products.

As a Lead Software Engineer-Databricks at JPMorgan Chase within our Corporate Sectors Enterprise Technology team, 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 firms business objectives.

       Job responsibilities

• Lead the architecture and delivery of high-throughput, low-latency data pipelines on Databricks using Apache Spark (Core, SQL, Structured Streaming), driving performance, reliability, and scalability.
• Establish and evolve Lakehouse patterns with Delta Lake (ACID transactions, schema evolution, time travel, Z-ordering, compaction) to ensure performant, maintainable data platforms at scale.
• Own Databricks cluster strategy and configuration, including runtime selection, autoscaling, driver/executor sizing, Spark configurations, init scripts, cluster policies, pools, and instance profiles.
• Orchestrate and automate pipelines and jobs using Databricks Workflows, integrating with AWS eventing and orchestration services as needed.
• Design secure ingestion and transformation frameworks leveraging Databricks services, including Delta or unmanaged table design, ingestion task creation, and Airflow DAGs to produce trusted and refined datasets.
• Enforce data quality, lineage, and governance using Unity Catalog and/or AWS Glue Catalog, embedding expectations and validation directly into pipelines.
• Drive Spark and Databricks performance engineering and tuning (partitioning and file sizing, AQE, broadcast joins, shuffle tuning, caching, spill/memory control, job right-sizing, and liquid clustering/partitioning keys) to optimize cost and throughput.
• Build and maintain reusable libraries, frameworks, and APIs in Python and/or Java, ensuring strong unit, integration, and data validation test coverage.
• Implement CI/CD for data projects using Git-based workflows, Terraform-based infrastructure deployments and environment promotion, and automated releases; champion engineering standards, code reviews, and enterprise-authorized AI-assisted engineering practices (e.g., code review/refactoring, test acceleration, and incident/root-cause analysis) with consistent validation (secure coding, peer review, automated testing) and reuse of proven patterns.
• 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.
   

  Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience. 
• Advanced experience in software engineering and data engineering, including significant production delivery with Apache Spark on Databricks and/or AWS EMR.
• Advanced hands-on Databricks expertise across Delta Lake, Unity Catalog, Workflows, Repos/notebooks, and SQL Warehouses, including cluster configuration and optimization.
• Proven ability to architect, build, and operate reliable ETL/ELT data pipelines (batch and streaming), including schema design/evolution, SLAs, and reliability engineering practices.
• Deep Spark performance tuning skills, with experience diagnosing bottlenecks and optimizing jobs for scalability, cost, and runtime efficiency.
• Strong programming proficiency in Python and/or Java for data processing, platform tooling, and automation.
• Strong SQL and analytics data modeling expertise, including dimensional/star schema design and Lakehouse best practices.
• Demonstrated experience leading effective use of approved AI-assisted software development tools (coding, code review, test acceleration, troubleshooting), including setting team expectations and validation standards for correctness, performance, and security of AI outputs.
• Strong responsible-AI and security-first engineering mindset, including data sensitivity awareness, secure handling of inputs/outputs, roles/instance profiles, secrets management, encryption at rest/in transit, network controls, and adherence to resiliency and security expectations; experience coaching teams on safe, compliant adoption within delivery practices.
• 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
  
  Preferred qualifications, capabilities, and skills
• Experience with Delta Live Tables and advanced governance (catalogs, grants, auditing) in Databricks.
• AWS networking knowledge (VPC, subnets, routing, security groups) and data egress controls.
• Experience with Terraform for Infra deployments
• Cost optimization experience: autoscaling strategies, spot vs on-demand, auto-termination, storage layouts and compaction.
• Familiarity with Airflow, Genie, Streamlit and React
• Observability for data systems (freshness/completeness metrics, lineage, SLAs, alerting).
• Demonstrated leadership in code quality, reviews, testing strategy, CI/CD, and technical mentorship; excellent communication with stakeholders.
   

Design, build, and deliver secure, scalable ML models that support firmwide financial decision-making.