Own the AI data product roadmap and translate business priorities into a sequenced backlog of reusable data products with defined ownership, service levels, and lifecycle controls
Partner with dashboard, analytics, and AI delivery teams to identify reporting assets that should evolve into governed AI-ready data products
Establish data product charters defining purpose, consumers, source ownership, data contracts, access models, and retention requirements
Implement table-, column-, and row-level access standards, classification, audit trails, and data handling controls using Databricks Unity Catalog and AWS services
Lead the design and operation of incremental, observable, and cost-efficient data ingestion and curation pipelines across enterprise data sources
Create and maintain versioned, reproducible training datasets, evaluation datasets, inference inputs, feature tables, and monitoring datasets with clear metadata and documentation
Define and implement AI-specific data quality standards including completeness, consistency, timeliness, uniqueness, referential integrity, distribution stability, and drift sensitivity
Establish automated quality controls and enforce readiness gates that prevent model training, deployment, or production promotion when critical data quality requirements are not met
Develop operational runbooks for recovery, backfills, reprocessing, incident response, and escalation management
Participate in operational reviews and drive root-cause analysis and corrective actions to improve platform reliability
Optimize storage, compute utilization, partitioning, scheduling, and overall platform cost efficiency
Approve data readiness gates for model training, evaluation, deployment, and significant production inference input changes
Escalate data ownership, quality, access, and cost issues that introduce risk or impede business outcomes
Drive dashboard-to-data-product transformations and establish governed, reusable data products that support critical AI use cases
Deliver quality and cost scorecards that provide transparency into data health, reliability, usage, and platform cost drivers
Bachelor’s degree or higher in Computer Science, Data Science, AI/ML, Applied Mathematics, Engineering, or a related field
8+ years of experience in data engineering, data management, AI data foundations, or related technology leadership roles
Demonstrated ownership of data products or mission-critical data pipelines delivering measurable business impact
Hands-on experience with AWS, Databricks, Delta Lake, Unity Catalog, data lineage, observability, and production support
Experience defining and managing data contracts, quality gates, service levels, and data access models
Strong stakeholder management skills with experience partnering across business leaders, product owners, data and AI engineering teams, security, and platform organizations
Ability to operate at a senior level, challenge incomplete requirements, evaluate trade-offs, and drive decisions in ambiguous environments
Strong communication skills with the ability to simplify complex technical concepts for business stakeholders
Proven ownership mindset with accountability for business and technical outcomes
Strong judgment balancing speed, quality, security, cost, and maintainability
Excellent problem-solving skills and ability to adapt in fast-paced, evolving environments
Commitment to data quality, operational excellence, documentation, and continuous improvement
