Lead Software Engineer

JPMorgan Chase · ✓ Verified company
📍 Bengaluru, Karnataka, India · On-site · Full-time

About the role

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.