> Markdown version of [/jobs/ext/3420696-lead-software-engineer-databricks-spark-aws](https://www.wearedevelopers.com/jobs/ext/3420696-lead-software-engineer-databricks-spark-aws). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Software Engineer - Databricks/Spark/AWS - **Company:** JPMorgan Chase & Co. - **Location:** Columbus, OH, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Amazon S3, Automation of Tests, Software Quality, Code Review, Continuous Integration, Data Validation, Information Engineering, Extract Transform Load (ETL), Data Security, Data Systems, Amazon DynamoDB, Identity and Access Management, Subnetting, Junit, Python (Programming Language), Routing, Performance Tuning, Reliability Engineering, Software Tools, Standard Sql, Secure Coding, Software Engineering, SQL Databases, Data Streaming, Strategies of Testing, Toolchain, Data Logging, Data Processing, Apache Spark, Caching, Amazon Virtual Private Cloud (VPC), Pytest, Data Lakes, Git Flow, Deployment Automation, Star Schema, Apache Kafka, Cloudwatch, Terraform, Code Restructuring, Data Pipelines, Serverless Computing, Databricks - **Published:** September 11, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=126b625af9e21e8b ## About the Role * Formal training or certification on software engineering concepts and 5+ years applied experience. * 10+ years of professional software/data engineering experience, including substantial production work with Spark on Databricks or EMR. * Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security. * Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices * Strong proficiency in Python and/or Java for data processing, platform tooling, and automation. * Hands-on Databricks expertise (Delta Lake, Unity Catalog, Workflows, Repos/notebooks, SQL Warehouses). * Solid AWS experience: S3, IAM, Glue, CloudWatch, Kinesis / MSK, DynamoDB * Proven track record architecting and operating ETL/ELT pipelines (batch and streaming), with schema design/evolution, SLAs, and reliability engineering. * Deep skills in Spark performance tuning and Databricks cluster setup/optimization. * Strong SQL and analytics data modeling (dimensional/star schema; lakehouse best practices). * CI/CD and automation tooling for data (Git workflows, artifact management) and testing frameworks (pytest, JUnit). * Security-first mindset: roles/instance profiles, secret management, encryption-at-rest/in-transit, and network controls. Preferred qualifications, capabilities, and skills: * Experience with Delta Live Tables and advanced governance (catalogs, grants, auditing) in Databricks. * AWS networking knowledge (VPC, subnets, routing, security groups) and data egress controls. * Experience with Terraform for Infra deployments * Cost optimization experience: autoscaling strategies, spot vs on-demand, auto-termination, storage layouts and compaction. * Familiarity with Kafka/MSK or Kinesis Data Streams/Firehose for real-time ingestion. * Observability for data systems (freshness/completeness metrics, lineage, SLAs, alerting). * Demonstrated leadership in code quality, reviews, testing strategy, CI/CD, and technical mentorship; excellent communication with stakeholders. ## Description * Lead architecture and delivery of high-throughput, low-latency data pipelines using Databricks and Apache Spark (Core, SQL, Structured Streaming). * Establish lakehouse patterns with Delta Lake (ACID transactions, schema evolution, time travel, Z-ordering, compaction) and ensure performance at scale. * Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team. * Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation. * Own Databricks cluster strategy and setup: runtime selection, autoscaling, driver/executor sizing, Spark configs, unit scripts, cluster policies, pools, and instance profiles. * Orchestrate jobs with Databricks Workflows; integrate with AWS eventing and orchestration as needed. * Design secure data ingestion and transformation frameworks leveraging AWS services: + S3 for data lake storage and lifecycle management + Glue for catalog/metadata and ETL jobs + IAM and Secrets Manager for role-based access and credential management + CloudWatch for logging, metrics, and alerting + Lambda for serverless utilities + Kinesis and/or Kafka/MSK for streaming ingestion * Enforce data quality, lineage, and governance using Unity Catalog and/or Glue Catalog; embed expectations and validation into pipelines. * Drive Spark performance engineering: partitioning strategies, file sizing, AQE, broadcast joins, shuffle tuning, caching, spill/memory control, and job right-sizing to optimize cost. * Build reusable libraries, frameworks, and APIs in Python and/or Java; oversee unit, integration, and data validation testing. * Implement CI/CD for data projects (Git-based workflows), Terraform Infrastructure deployments environment promotion, and automated deployments; champion engineering standards and code reviews. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [pytest: Simple, rapid and fun testing with Python](https://www.wearedevelopers.com/videos/213-pytest-simple-rapid-and-fun-testing-with-python) - [How Unit Testing Saved My Career](https://www.wearedevelopers.com/videos/1642-how-unit-testing-saved-my-career) - [HTTP headers that make your website go faster](https://www.wearedevelopers.com/videos/1676-http-headers-that-make-your-website-go-faster) - [Agents for the Sake of Happiness](https://www.wearedevelopers.com/videos/1387-agents-for-the-sake-of-happiness) - [From boy scouting to redrawing the landscape](https://www.wearedevelopers.com/videos/1140-from-boy-scouting-to-redrawing-the-landscape) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know)