Build the data foundations that enable trusted insights, stronger controls, and better outcomes across the firm through scalable, well-governed data platforms.

As a Lead Software Engineer at JPMorganChase within Corporate Technology’s Data Governance and Controls Technology team, you will design and deliver secure, resilient, and high-performing data solutions that improve how data is governed, accessed, and used. You will partner across business, analytics, and technology teams, while mentoring engineers and setting technical standards that raise quality and reliability at scale.

Job responsibilities

• Design and deliver scalable batch data pipelines with strong performance, fault tolerance, and end-to-end observability.
• Develop and operate workflow orchestration to schedule, monitor, and manage data movement and transformations with clear operational ownership.
• Translate complex business requirements into technical solutions aligned to data lake and data warehousing standards.
• Establish and maintain governance processes for data modeling, cataloging, ownership, lineage, and access control to improve trust and transparency.
• Optimize large-scale distributed processing workloads through performance tuning, efficient storage/compute patterns, and resilient runtime design.
• Mentor engineers through technical reviews, standards, and knowledge sharing to strengthen engineering excellence and consistency.
• Perform advanced quantitative analysis on large datasets to uncover trends and drive data-informed decisions.
• Uses enterprise-authorized AI capabilities within the work environment to accelerate design comprehension and pipeline analysis (e.g., drafting documentation), validating outputs and handling data according to sensitivity and security requirements.
• Applies reuse-first, AI-assisted practices within SDLC/toolchain automation to reduce manual toil while maintaining security, resiliency, and traceability/auditability expectations.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience
• 5+ years of data engineering experience building and operating production-grade data pipelines in cloud environments.
• Deep expertise with Amazon Web Services data platforms and data lake architectures, including hands-on experience with Ab Initio.
• Hands-on experience with modern data lake and warehousing technologies (for example, Databricks and Snowflake) and processing engines such as Spark and Flink.
• Proficiency in SQL and experience with Oracle Database plus one or more scripting/programming languages (for example, Java, Perl, or Unix scripting).
• Experience applying Agile delivery practices, including prioritizing backlogs and leading team ceremonies to drive continuous improvement.
• Experience with large-scale distributed data processing, performance tuning, and reliability practices (monitoring, alerting, and incident response readiness).
• Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, testing, troubleshooting, or documentation) with demonstrated ability to critically evaluate and validate AI-generated outputs.
• 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

Preferred qualifications, capabilities, and skills

• Experience with data modeling using Erwin.
• Amazon Web Services certification and/or Databricks certification.
• 8+ years of overall experience, including a strong track record as an individual contributor and demonstrated experience leading technical direction for teams.
• Experience implementing data governance capabilities (for example, data cataloging, lineage, stewardship workflows, and access controls) in enterprise environments.