Senior AI Data Product Owner

Job Description :

Role : Senior AI Data Product Owner.

Role Objective :

Own the end-to-end lifecycle of AI data products on AWS and Databricks.

Transform existing dashboards and ad-hoc datasets into governed, reusable, production-grade data products trusted for AI training, evaluation, inference, monitoring, and continuous improvement.

The role guides client Sensors from a dashboard-oriented data culture toward an AI-first operating model where data products are managed as enterprise assets, not one-off project outputs.

Core Responsibilities : AI Data Product Strategy and Ownership :

• Own the AI data product roadmap; translate business priorities into a sequenced backlog of reusable data products with clear owners, 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.

Data Product Operating Model and Governance :

• Establish product charters defining purpose, consumers, source ownership, data contracts (schema, semantics, freshness, quality expectations, change notification), access models, and retention rules.

• Implement table-, column-, and row-level access standards, classification, audit trails, and handling rules via Databricks Unity Catalog and AWS controls.

AI Data Ingestion, Curation, and Productization :

• Lead design and operation of incremental, observable, cost-aware ingestion and curation pipelines from enterprise sources into AWS and Databricks.

• Create versioned, reproducible training sets, evaluation sets, inference inputs, feature tables, and monitoring datasets with discoverable documentation and metadata.

Data Quality Engineering and AI Readiness Gates :

• Define AI-specific quality dimensions (completeness, consistency, timeliness, uniqueness, referential integrity, distribution stability, drift sensitivity) with automated checks.

• Enforce hard gates that block training, deployment, or production promotion when critical quality checks fail.

Production Operations, Support, and Continuous Improvement :

• Provide runbooks for recovery, backfills, reprocessing, incidents, and escalation.

• Participate in operational reviews and drive root-cause corrective actions.

• Optimize storage, compute, partitioning, scheduling, and cost transparency across the platform.

Decision Rights :

• Approve data readiness gates before model training, evaluation, deployment, or material changes to production inference inputs.

• Escalate data ownership, quality, access, or cost issues that block business outcomes or introduce operational risk.

Key Deliverables : KPI :

• Expected outcome.

Dashboard-to-data-product transformations :

• 1. >= 2 major transformations per year.

AI data product coverage :

• 1. All critical AI use cases backed by named, governed, reusable data products.

Dataset reproducibility :

• 1. Training, evaluation, and inference datasets are versioned, documented, and quality-gated.

Data quality visibility :

• 1. Issues measured, assigned to owners, and trending downward quarter-over-quarter.

Quality and cost scorecards :

• 1. Transparency on data health, usage, reliability, and platform cost drivers.

Qualifications And Experience :

• Education : Bachelor's or higher in Computer Science, Data Science, AI/ML, Applied Mathematics, Engineering, or related field.

• Experience : 8+ years in data engineering, data management, AI data foundations, or adjacent technology leadership with proven ownership of data products or critical pipelines delivering measurable business impact.

• Technical : Hands-on with AWS, Databricks, Delta Lake, Unity Catalog, data lineage, observability, and production support.

• Experience defining data contracts, quality gates, service levels, and access models.

• Stakeholder management : Track record working across business leaders, product owners, data/AI engineers, security, and platform teams.

• Ability to operate at senior level, challenge incomplete requirements, make trade-offs transparent, and drive decisions when ownership is unclear.

Preferred Certifications :

• Databricks Data Engineer Professional or Databricks Machine Learning Professional.

• AWS Certified Data Analytics, AWS Solutions Architect Associate, or AWS Solutions Architect Professional.

• Relevant data governance, data management, product ownership, or agile delivery certifications are a plus.