Lead Software Engineer

Job Description

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 JPMorgan Chase as a part of Consumer and community banking technology team, 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.

Job Responsibilities

• Lead evaluation sessions with external vendors, startups, and internal teams to probe architectural designs, technical credentials, and applicability within existing systems and information architecture.
• Drive team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes, while establishing validation standards and promoting reuse of effective patterns.
• Apply knowledge of tools within the Software Development Life Cycle toolchain, including AI-assisted development and automation capabilities, to improve value realized by automation.
• Lead architecture and engineering of large-scale data processing and platform solutions using Python, and Java.
• Design and implement robust ETL/ELT pipelines, including ingestion, transformation, validation, reconciliation, and publishing across curated layers.
• Build and operationalize Medallion architecture patterns for data quality, lineage, governance, and reuse.
• Develop and optimize solutions on Data Lakes partitioning strategies.
• Ensure engineering best practices: code quality, testing, CI/CD, observability, security-by-design, and operational readiness.
• Drive performance optimization across Spark jobs (shuffle tuning, joins, caching, skew handling), storage layout, and Snowflake workloads.
• Partner with product owners, architects, data governance, and downstream consumers to translate requirements into resilient technical solutions.
  
  

Required Qualifications, Skills, And Capabilities

• Formal training or certification on software engineering concepts and 5+ years of applied experience.
• Demonstrated experience leading effective use of approved AI-assisted software development tools, including setting expectations for validating outputs for correctness, performance, and security.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices.
• Strong hands-on development skills in Python and/or Java (ideally both).
• Strong experience with Apache Spark and distributed data processing concepts.
• Proven expertise building ETL/ELT pipelines and data integration frameworks.
• Strong understanding of data storage/serialization and table/file formats, including Parquet and Avro.
• Deep understanding of Big Data ecosystem fundamentals (distributed compute, fault tolerance, partitioning, data quality, metadata management).
• Strong experience implementing Medallion architecture and Data Lake design principles.
• Strong working knowledge of Snowflake including loading/unloading patterns and performance considerations.
• Ability to lead technical decisions, drive alignment across teams, and communicate clearly with technical and non-technical stakeholders.
  
  

Preferred Qualifications, Skills, And Capabilities

• Experience with data orchestration frameworks and pipeline automation.
• Experience with data governance concepts (lineage, cataloging, access controls, PII handling) and production operations.
• Exposure to streaming/event-driven patterns and incremental processing strategies.
• Experience designing reusable data products, frameworks, or platform components used by multiple teams.
• Domain experience in highly regulated environments (risk, audit, compliance, privacy).
• Experience with lakehouse patterns, table formats (e.g., ACID table layers), and data platform modernization programs.
• Experience with cost optimization and FinOps-style controls for big data workloads.
  
  

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