In this role, you’ll make an impact in the following ways: Help formulate the RCAR data platform strategy, reference architecture, and capability roadmap; define standards for data models, canonical forms, and semantic layers. Define and implement RCAR’s end-to-end data architecture (ingest, transform, serve) and AI-enabled infrastructure to power self-serve analytics, advanced analytics, and regulatory reporting, anchored in SLAs/SLOs, observability, and resilience (DR/BCP). Establish canonical, vectorized data forms and standardized semantic layers to accelerate feature engineering, retrieval-augmented generation (RAG), and reusable analytics patterns. Build and govern a comprehensive metadata program—including catalog, lineage capture, classification, ownership (RACI), stewardship, and approval workflows—to improve discovery, traceability, and audit-readiness. Design and operationalize a data intelligence mesh that balances federated domain data products with centralized guardrails for quality, security, and interoperability; define shared contracts and interoperability standards. Embed robust access and trust controls (segregation of duties, break-glass protocols) ensuring compliant use of Risk & Compliance data; align with privacy, retention, and acceptable use requirements. Partner with CDO and Data Engineering to deliver scalable pipelines, data contracts, and runtime governance—improving reliability, timeliness, and cost efficiency via FinOps practices. Enable responsible AI by integrating feature stores, embedding/vector stores, model registries, monitoring, and explainability across the data and ML lifecycle; enforce model/data versioning and reproducibility. Elevate data quality through critical data element (CDE) governance, validation rules, profiling, anomaly detection, and proactive remediation workflows; publish data quality scorecards and ownership. Provide executive-ready data health reporting and platform metrics that link infrastructure performance to business outcomes, risk posture, and regulatory commitments; maintain clear decision logs and evidence packs. Align platform strategy with BNY’s strategic pillars—be more for our clients through usable data products, run our company better with automation and controls, and power our culture through collaboration and standards. To be successful in this role, we’re seeking the following: 8-10 years in in data architecture, platform engineering, or data product leadership; experience in financial services and Risk/Compliance domains strongly preferred. Proven expertise in enterprise data design (dimensional/normalized models), canonical data forms, vectorization/embeddings, semantic layers, and distributed data systems. Hands-on experience with metadata cataloging, lineage, data governance, and access controls across regulated environments. Familiarity with AI/ML platform components (feature stores, embedding/vector databases, model registries, monitoring/explainability) and MLOps/DataOps practices. Strong track record partnering with CDO and Data Engineering organizations to deliver scalable, reliable data platforms and reusable data products. Proficiency with SQL, data warehousing/data lakes, streaming, orchestration, and observability tools; familiarity with Python and ML pipelines is advantageous. Excellent communication and stakeholder management skills—able to translate technical concepts into business outcomes and influence senior leaders. Intellectual curiosity, enthusiasm, and adaptability to rapidly assimilate new information and operate with a strong delivery focus in a fast-paced environment. Bachelor's degree required; advanced degree in a quantitative areas like business, finance, engineering, or related disciplines; relevant industry certifications or qualifications are a plus.