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Lead Software Engineer - Databricks, ML, AWS
lead · Technology / Software Development
We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.
As a Lead Software Engineer, Machine Learning and Cloud at JPMorgan Chase within the Corporate Technology- Consumer & Community Bank Finance group, 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 and lead, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Job Responsibilities:
- 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 Databricks services: Design delta or unmanaged tables, Create tasks for data, ingestion process, Create DAGs using Airflow to orchestrate creation of trusted and refined data.
- 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.
Required qualifications, capabilities, and skills:
- Formal training or certification on software engineering concepts and 5+ years applied experience.
- 8+ 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).
- 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.
- Observability for data systems (freshness/completeness metrics, lineage, SLAs, alerting).
- Drive databricks performance tuning through liquid clustering or partitioning keys, familiarity with Airflow, Genie, Streamlit and React
- Demonstrated leadership in code quality, reviews, testing strategy, CI/CD, and technical mentorship; excellent communication with stakeholders.