We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at JPMorganChase within the Corporate Technology , you are 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 firm’s business objectives.

In this position you will drive efficiency and cost optimization across our Databricks platform. This role will partner with application teams, and data stakeholders to ensure Databricks workloads are high-performing, cost-efficient, and aligned with enterprise governance standards.

The ideal candidate is a Databricks expert with deep experience in Spark performance tuning, DBU optimization, Delta Lake best practices, cluster governance, and workload rightsizing across large, complex environments. This role is critical in shaping our Databricks FinOps strategy.

Job Responsibilities:

Analyze DBU consumption trends to identify inefficiencies across clusters, SQL warehouses, and jobs.Develop cluster & SQL warehouse rightsizing strategies using real workload data.Implement and govern cluster policies to enforce optimal worker types, runtimes, and configuration standards.Build automated monitors for idle clusters, runaway jobs, underutilized warehouses, and inefficient workloads.Optimize cloud foundations (EBS/S3 performance).Identify areas of cost leakage and provide recommendation on right compute strategy to resolve it.Continuously work with Databricks product teams to enable vendor provided tools and techniques for cost monitoring and optimization.

 

Required qualifications, capabilities, and skills

Formal training or certification on software engineering concepts and 5+ years applied experience.Databricks Certified Data Engineer ProfessionalAct as the primary SME for Databricks performance and cost optimization.Partner with application teams to identify inefficiencies and rewrite pipelines.Create playbooks, runbooks, and enterprise standards for Databricks engineering.Tune PySpark/Scala workloads to minimize shuffle, improve parallelism, and reduce job runtimes.Implement best practices for caching, broadcast joins, and data layout.Modernize legacy jobs with Workflows and Delta Live Tables for reliability and automation.Standardize job orchestration patterns and eliminate inefficient pipeline designs.Provide application teams with guidance on writing performant Spark code.Build reports using System Tables, logging, tags, and FinOps metrics.

 

Preferred qualifications, capabilities, and skills

Strong experience with cost attribution, tagging, budgeting, and rightsizing.Experience building dashboards for cost and performance reporting.Excellent communicator with ability to challenge teams diplomatically.Strong documentation & coaching skills.Can drive enterprise-wide change and enforce consistent standards.Experience in large-scale regulated enterprise environments.