About This Role
This lead data engineering role at JPMorganChase focuses on strategic data provisioning within Asset and Wealth Management, enhancing and delivering secure and scalable data solutions. The position suits a core technical contributor responsible for maintaining critical data pipelines and architectures across various business functions.
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
- 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
- Use 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
- Apply 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
