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# Lead Software Engineer

- **Company:** JPMorgan Chase & Co.
- **Location:** Jersey City, NJ, United States
- **Experience:** Expert
- **Salary:** $156,750.0 - $215,000.0
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Amazon S3, Software Quality, Code Review, Continuous Integration, Data Validation, Information Engineering, Data Governance, Identity and Access Management, Metadata, Networking Basics, Performance Tuning, Data Streaming, Data Logging, Data Classification, Apache Spark, Caching, Amazon Virtual Private Cloud (VPC), Data Lakes, Integration Tests, Data Management, Terraform, Software Version Control, Data Pipelines, Databricks
- **Published:** September 30, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=28c97ad76408bb13

## About the Role

* 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.
*

## Description

* 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.