As a Lead Software Engineer at JPMorgan Chase within the Corporate Technology, youare 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 firm27s business objectives.

Job responsibilities

• Architect the lake house: design bronze/silver/gold (or equivalent) layers, domain data products
• Deliver ingestion at scale: implement resilient ingestion from AWS sources into Databricks (batch + streaming), including CDC where needed.
• Build maintainable pipelines: use Delta Live Tables (DLT) and/or standard Jobs with clear modular structure, testing, and documentation.
• Operational excellence: productionize workloads via Databricks Workflows/Jobs, robust retries, checkpointing, idempotency, and safe re-runs.
• Governance by design: enforce least privilege, data classification (PII), auditing, lineage/metadata, and controlled sharing/consumption.
• 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.
• Performance & cost management: tune Spark/Delta workloads, right-size clusters, optimize storage layout, and manage job/warehouse spend.
• Lead and mentor: set engineering standards, run design reviews, drive code quality, and upskill engineers in Spark/Databricks best practices, cross-functional delivery: translate stakeholder needs into technical plans, communicate tradeoffs, and align with security/platform teams.
• CI/CD and IaC: Terraform (preferred) for Databricks + AWS resources; promotion across environments.
• Testing: unit/integration tests for transformations, data quality checks, contract testing, and replay/backfill procedures, version control & code review discipline; clear documentation and runbooks.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years hands on Software Development Life Cycle experience
• Strong data engineering experience, including proven leading delivery/architecture for multi-team data platforms.
• Hands-on experience building and operating a Databricks Lakehouse Hosted in AWS
• Deep experience with Delta Lake (ACID tables, partitioning, schema evolution,
• Proven experience with Spark on Databricks (performance tuning, cluster sizing, skew mitigation, joins, caching, file sizing).
• Experience with streaming and batch pipelines (Structured Streaming; incremental processing; backfills; late-arriving data).
• Strong AWS fundamentals for data platforms: S3, IAM, KMS, networking basics (VPC/security groups), logging/auditing.
• Experience implementing data governance/security controls in Databricks (e.g., Unity Catalog, table/column permissions, credential passthrough patterns as applicable).

• 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

• Demonstrated ownership of reliability: monitoring/alerting, incident response, RCA, and SLO/SLA management.