As a Director of Software Engineering at JPMorganChase within the Infrastructure Platforms (IP) Foundation Services Data Protection & Recovery Product Line, you will lead a critical data engineering function responsible for safeguarding and managing the firm's most important data assets. You will drive the strategy, design, and delivery of data solutions that support enterprise-scale backup, recovery, resiliency, and regulatory compliance capabilities across the firm.
We are seeking a technology leader who combines strong software engineering fundamentals with hands-on data engineering experience. The ideal candidate has a proven track record of solving complex business and technical problems, influencing stakeholders across organizations, and delivering scalable data solutions that drive measurable business outcomes.
Job Responsibilities
- Lead the design, development, and implementation of large-scale data engineering solutions supporting the Data Protection & Recovery Product Line.
- Drive initiatives focused on data lineage, data quality, data completeness, governance, and data lifecycle management across multiple platforms and applications.
- Partner with engineering, product, architecture, infrastructure, and operations teams to identify and solve complex data challenges across upstream and downstream systems.
- Establish technical strategy, architecture direction, engineering standards, and best practices for data engineering initiatives.
- Lead teams responsible for building scalable data pipelines, datasets, data services, and reporting capabilities supporting critical business functions.
- Deliver technical solutions that can be leveraged across multiple businesses, platforms, and technology domains.
- Drive engineering excellence through modern software development practices, automation, resiliency, and operational rigor.
- Influence senior technology leaders and stakeholders while balancing strategic objectives with practical execution.
- Ensure solutions meet firmwide security, risk, compliance, resiliency, and regulatory requirements.
- Lead adoption of AI-enabled engineering practices and automation capabilities to improve delivery speed, quality, and operational outcomes.
- Develop, coach, and mentor engineering talent while fostering a culture of innovation, accountability, collaboration, and continuous improvement.
Required Qualifications, Capabilities, and Skills
- Formal training or certification in Software Engineering, Computer Science, Computer Engineering, Mathematics, or a related technical field, or equivalent practical experience.
- 10+ years of applied software engineering experience designing and delivering complex technology solutions.
- Strong hands-on software engineering background with experience developing applications using Java, Python, and/or other modern programming languages.
- Experience designing, building, and supporting large-scale data platforms, data pipelines, and distributed systems.
- Strong understanding of: Data lineage, Data quality, Data completeness, Data governance, Metadata management, Data lifecycle management
- Experience managing large datasets and building modern data lake and analytics solutions.
- Strong problem-solving skills with the ability to diagnose and resolve complex technical and data-related challenges.
- Experience working across multiple applications and systems with an understanding of upstream and downstream dependencies.
- Experience leading cross-functional teams of technologists across engineering, product, and platform organizations.
- Practical cloud-native engineering experience.
- Knowledge of responsible AI practices and governance considerations within software engineering and data environments.
Preferred Qualifications, Capabilities, and Skills
- Experience with Databricks, Snowflake, or similar modern data platforms.
- Experience working with Oracle, MongoDB, Cassandra, or other enterprise-scale database technologies.
- Experience building data products, self-service data capabilities, or enterprise data platforms.
- Experience supporting backup, recovery, storage, resiliency, or data protection platforms.
- Familiarity with data observability, data cataloging, and metadata management tools.
- Experience implementing AI-assisted software development and engineering automation capabilities.
- Strong understanding of operational resiliency, disaster recovery, and enterprise data management practices.