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Lead Data Engineer - Strategic Data Provisioning Data Engineer
lead · Data Science & Analytics
Join us as we embark on a journey of collaboration and innovation, where your unique skills and talents will be valued and celebrated. Together we will create a brighter future and make a meaningful difference.
As a Lead Data Engineer at JPMorganChase within the Asset and Wealth Management, you are an integral part of an agile team that works to enhance, build, and deliver data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. As a core technical contributor, you are responsible for maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
- Make data available for AI and analytics initiatives, working closely with use case owners to define requirements, manage product dependencies, and support agile product routines that oversee cross-product data dependencies and prioritize delivery
- Collaborate with business, technology, and operations partners to understand data requests and accelerate provisioning through deployment of "AI for Data"
- Provide transparency and drive executive visibility into bottlenecks, progress, performance metrics, and adoption tracking in making AI-ready and critical data sources available for innovation
- Identify the lineage and provenance of critical data assets to support governance, regulatory, and business requirements. Embed evergreen controls on data flows to improve safety, transparency, and traceability while meeting regulatory requirements
- Develop and deliver data lineage analysis and documentation that provides executive visibility on progress meeting critical SLAs (including blockers, resourcing, etc.)
- Drive insight into areas of efficiency and risk through consolidation and reengineering of data flows
- Lead data quality issue root cause analysis using deep data profiling and advanced analytics techniques, then fix the cause and embed uplifted evergreen controls to prevent future failures
- Develop proactive controls to reduce the time from data quality issue identification to resolution, improving client experience and driving operational efficiency through elimination of cost of poor quality (COPQ)
- Demonstrate control environment improvements and reduction in toil to achieve benefits through common tooling and frameworks. Uplift the metadata (semantic layer) of existing data ("Brownfield" enrichment) to support AI and Natural Language Query (NLQ) usage, accelerate adoption of Mesh data architecture, reduce consumer friction from poor catalog quality, and deliver data product prototypes that demonstrate the value of uplifted data assets
- Uses enterprise-authorized AI capabilities within the work environment to accelerate data platform and model design analysis and documentation, validating outputs and handling data according to sensitivity and security requirements.
- Applies reuse-first, AI-assisted practices within delivery and operational routines (e.g., backup/recovery validation and access control review support), ensuring traceability/auditability and alignment to resiliency and security expectations.
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5+ years applied experience
- Deep subject matter expertise in wealth and asset management, covering customer, account, position, transaction, and/or reference data domains
- Proven execution ability in a matrixed and complex environment with the ability to influence people at all levels of the organization
- Experience in strategic or transformational change initiatives, including data governance, data quality, or analytics transformation programs.Strong technical skills in data profiling, analysis, and data management using modern tools and environments (Python, R, SQL, Spark, cloud platforms)
- Understanding of data lineage concepts and experience with lineage analysis, metadata management, and data cataloging
- Excellent communication skills with the ability to convey complex technical concepts to diverse audiences including executive leadership. Ability to work in a highly collaborative and intellectually challenging environment
- Willingness to challenge the status quo, think creatively, problem-solve, and drive innovation
- Experience with data quality frameworks, including profiling, rule development, issue remediation, and preventative controls
- Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support data engineering workflows with strong validation habits and awareness of data sensitivity.
- Ability to review and validate AI-assisted outputs (e.g., model/design summaries or operational checklists) before use, escalating when uncertain and following data handling requirements.
- Strong proficiency in data science and analytics tools: Python, R, SQL, Spark, and cloud data platforms (AWS, Azure, GCP). Experience with data visualization and reporting tools (e.g., Tableau, Power BI) to deliver executive dashboards and performance metrics
- Hands-on experience with data lineage tools and techniques, including graph databases and metadata management platforms. Knowledge of data governance frameworks, data quality dimensions, and regulatory requirements (e.g., BCBS 239, GDPR)
- Experience with AI/ML technologies and their application to data management challenges (e.g., automated data profiling, metadata enrichment). Understanding of agile and product management methodologies and experience working in agile teams
- Ability to multi-task in a fast-paced environment and operate independently with minimal supervision.Strong judgment with the ability to balance strategic vision with pragmatic, incremental delivery
- Experience building and growing capabilities and developing talent in data science or data management teams
- Excellent interpersonal skills and ability to build strong working relationships with business, technology, and control stakeholders across global teams