Join us as we embark on a journey of collaboration and innovation, where your unique skills and talents will be valued and celebrated. Together we will create a brighter future and make a meaningful difference.
As a Lead Data Engineer at JPMorganChase within the Corporate Technology, you are an integral part of an agile team that works to enhance, build, and deliver data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. As a core technical contributor, you are responsible for maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
- 7+ years in data engineering, including 2+ years 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 ownership of reliability: monitoring/alerting, incident response, RCA, and SLO/SLA management.
Required qualifications, capabilities, and skills
- 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.
- 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 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.