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As a Lead Software Engineer-Databricks at JPMorgan Chase within our Corporate 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 firm’s 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.

JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world’s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.

We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.

JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans