Director of Software Engineering-Data Protection & Recovery

If you are a software engineering leader ready to take the reins and drive impact, we’ve got an opportunity just for you. 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.