Lead Software Engineer - DataBricks, Spark, Terraform

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. As a Lead Software Engineer at JPMorganChase within the Corporate - Employee Platforms, 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. We use AI-assisted development as part of our day-to-day workflow, including GitHub Copilot for coding and native Databricks tools such as Databricks SQL Assistant and Genie to accelerate development, troubleshooting, and self-service analytics—while maintaining strong engineering controls and review practices. Job responsibilities Platform leadership & architecture — Define and drive the technical roadmap for our Databricks lakehouse/database platform (ingestion, storage, modeling, serving) with clear standards and reference patterns. Data modeling & database engineering — Design curated datasets (e.g., medallion architecture) using Delta Lake, dimensional/semantic modeling where appropriate, and enforce consistent naming, partitioning, and performance practices. Reliability & operations — Build for availability and predictable performance; establish SLOs, runbooks, alerting, incident response, and operational hygiene for pipelines and SQL workloads. Security, governance & access controls — Implement and maintain strong governance (e.g., Unity Catalog), least-privilege access, auditing, data classification, and lifecycle management. Performance & cost management — Tune Spark/SQL workloads, optimize clusters/warehouses, manage caching and storage patterns, and implement cost observability/chargeback as needed. Engineering excellence — Set standards for code quality, testing, CI/CD, branching strategy, documentation, and review. Establish reusable libraries/templates and enforce consistency across teams. Mentorship & collaboration — Coach engineers, lead design reviews, and partner with stakeholders to translate business needs into scalable data platform capabilities. 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 7+ years in data engineering/platform engineering, with hands-on Databricks experience in production environments. Strong proficiency in Spark (PySpark/Scala) and SQL , including performance tuning and troubleshooting. Proven experience building and operating data platforms : batch/stream ingestion, transformation frameworks, orchestration, and curated data layers. Experience with Data Lake, (DataBricks, SnowFlake, or AWS) table design, and optimization (partitioning, Z-ORDER, file sizing). Familiarity with Unity Catalog (or equivalent governance tooling): permissions, catalogs/schemas, lineage/auditing concepts. Solid software engineering fundamentals: Git, CI/CD, automated testing, code reviews , modular design, and documentation. Experience implementing observability (logs/metrics/traces), data quality checks, and monitoring for pipelines and SQL workloads. Strong communication skills and demonstrated ability to lead technical decisions across teams. 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 Databricks features: Workflows , Delta Live Tables (DLT) , Structured Streaming , Databricks SQL Warehouses . Transformation frameworks (e.g., dbt ) and semantic layer patterns. Infrastructure-as-code (e.g., Terraform ) and automated environment provisioning. Experience with regulated-data environments, privacy controls, and enterprise data governance programs.