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

>= 2 major transformations per year

AI data product coverage

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

Dataset reproducibility

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

Data quality visibility





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

Quality and cost scorecards

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.
- Preferred certifications: Databricks Data Engineer/ML Professional; AWS Data Analytics or Solutions Architect; data governance or agile delivery certifications are a plus.
- 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 Skilled.
- Relevant data governance, data management, product ownership, or agile delivery certifications are a plus.

📌 AI Data Product Owner (India)
🏢 Blue Cloud Softech Solutions
📍 India