Lead Software Engineer

JPMorgan Chase & Co.; Jersey City (NJ); On-site; USD 140,000 - 210,000; Full time

Job summary

JPMorganChase is seeking a Lead Data Engineer to join Corporate Technology. You will lead multi-team data platform delivery, architect scalable data pipelines, and own critical pipelines across business functions on a Databricks Lakehouse hosted on AWS.

The role emphasizes security, reliability, and governance. Responsibilities include designing bronze/silver/gold data layers, implementing streaming and batch ingestion, and mentoring engineers in Spark/Databricks best practices to meet firm

Qualifications

• 7+ years in data engineering with leadership experience.
• Hands-on Databricks Lakehouse on AWS.
• Deep Delta Lake, Spark and performance tuning.
• Experience with streaming and batch pipelines.
• Strong AWS fundamentals for data platforms.
• Knowledge of data governance and security controls.
• Ownership of reliability: monitoring, incident response, RCA.

Responsibilities

• Lead delivery/architecture for multi-team data platforms.
• Maintain and optimize critical data pipelines and architectures.
• Build and operate Databricks Lakehouse on AWS.
• Ensure secure, scalable data collection, storage, and analytics.
• Drive reliability with monitoring, incident response, and RCA.

Skills

Data engineering

Lead delivery

Team leadership

Databricks Lakehouse

Delta Lake

Spark on Databricks

Structured Streaming

AWS fundamentals

Data governance

Monitoring & incident response

Tools

Databricks

Delta Live Tables

AWS (S3, IAM, KMS)

Terraform

Unity Catalog

Spark

Delta Lake

Job description

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.