Senior 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.

• 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, 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
  • Cloud Watch 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.
• Mentor engineers, guide technical design reviews, and contribute to roadmaps and backlog prioritization; collaborate with product owners and cross‑functional teams.

• 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, Cloud Watch, 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.