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This listing doesn't post a salary. Most similar roles pay
$174,600–$219,425.
Based on 240 similar postings.
Employer
About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
JPMorgan Chase currently has
1262 open roles
on FindRole.
Listed pay typically runs
$150,433–$215,000
across 11 roles with salary data.
Lead Software Engineer - DataBricks, Spark, Terraform joins the Corporate - Employee Platforms team to build and deliver trusted, scalable technology products. This role involves defining the technical roadmap for a Databricks lakehouse platform, focusing on ingestion, storage, modeling, and serving. Key responsibilities include designing curated datasets using Delta Lake, managing security through Unity Catalog, and ensuring operational reliability via SLOs and incident response. The engineer will optimize Spark and SQL workloads while leading the adoption of AI-assisted development tools like GitHub Copilot. Required skills include 7+ years of experience with Databricks, Spark (PySpark/Scala), SQL, and CI/CD. The role requires expertise in data lake table design, observability, and infrastructure-as-code using Terraform to solve complex data platform engineering problems in regulated environments.
Define and drive the technical roadmap for the Databricks lakehouse platform including ingestion, storage, and modeling.
Design curated datasets using Delta Lake and enforce consistent naming, partitioning, and performance practices.
Establish SLOs, runbooks, alerting, and incident response for pipelines and SQL workloads.
Implement data governance, least-privilege access, auditing, and data classification using Unity Catalog.
Tune Spark/SQL workloads and optimize clusters, warehouses, and storage patterns for performance and cost.
Set standards for code quality, CI/CD, testing, and documentation while establishing reusable libraries.
Drive team adoption of AI-assisted engineering practices while establishing validation standards for secure coding and peer review.
What we're looking for
7+ years of experience in data engineering or 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 including batch/stream ingestion, transformation frameworks, orchestration, and curated data layers.
Experience with Data Lake table design and optimization using Databricks, Snowflake, or AWS.
Familiarity with Unity Catalog or equivalent governance tooling for permissions, lineage, and auditing.
Solid software engineering fundamentals including Git, CI/CD, automated testing, code reviews, modular design, and documentation.
Experience implementing observability, data quality checks, and monitoring for pipelines and SQL workloads.
Demonstrated experience leading the use of approved AI-assisted software development tools and coaching engineers on responsible AI use.
Experience with Databricks Workflows, Delta Live Tables, Structured Streaming, and Databricks SQL Warehouses (preferred).
Experience with transformation frameworks like dbt and semantic layer patterns (preferred).
Experience with Infrastructure-as-code like Terraform and automated environment provisioning (preferred).
Experience with regulated-data environments, privacy controls, and enterprise data governance programs (preferred).