Lead Software Engineer - Databricks, Spark, AWS

This is your chance to change the path of your career and guide multiple teams to success at one of the world's leading financial institutions.

As a Lead Software Engineer at JPMorgan Chase within Corporate Sector, Chief Technology Office, 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.

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.
• Own Databricks cluster strategy and setup: runtime selection, autoscaling, driver/executor sizing, Spark configs, init 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.
• Partner with platform security and networking teams to enforce encryption, network controls, and least-privilege access; ensure compliance with organizational policies.
• Lead incident response and root-cause analysis; establish SLAs, observability, and runbooks; drive continuous improvement in reliability and cost efficiency.

• 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.
• 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).
• Security-first mindset: roles/instance profiles, secret management, encryption-at-rest/in-transit, and network controls.
• Demonstrated leadership in code quality, reviews, testing strategy, CI/CD, and technical mentorship; excellent communication with stakeholders.

• 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.
• CI/CD and automation tooling for data (Git workflows, artifact management) and testing frameworks (pytest, JUnit).
• Observability for data systems (freshness/completeness metrics, lineage, SLAs, alerting).
• Experience in financial services or other regulated industries.