Senior AI Data Product Owner

POSITION TITLE

Senior AI Data Product Owner

LOCATION: Bangalore

Experience: 8+ years

BU Sensors, Transportation

BAND

Senior professional level, final band to be aligned with HR job architecture

Company

TE Connectivity Ltd. is a global technology and manufacturing leader creating a safer, sustainable, productive, and connected future. Our connectivity and sensor solutions support transportation, industrial applications, medical technology, energy, data communications, and the home. Every connection counts.

BU / Function Description The AI Transformation Center works as a strategic advisory and technology partner for Transportation Solutions and Sensors. The team partners with business functions shape digitalization, build AI-enabled solutions, and industrialize data foundations that make AI outcomes reliable, scalable, and secure.

The operating model is built around three pillars: consulting and AI advisory, AI solutioning and technology delivery, and project management with security and operational discipline.

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 TE 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 Qualified.
- 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.

Leadership and Cultural Fit
- Ownership mindset with clear accountability for outcomes, not only technical tasks.
- Strong judgement in balancing speed, quality, security, cost, and maintainability.
- Clear and concise communication across local and global stakeholders.
- Ability to simplify complex data topics for business leaders and turn strategy into executable delivery steps.
- Proactive problem solving in a fast-paced environment with changing priorities.
- High standards for data quality, operational discipline, documentation, and continuous learning.

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