Artificial Intelligence_Data Product Owner_Bangalore(Hybrid)

Lester Infoservices; Bengaluru; Hybrid; INR 2,500,000 - 3,500,000; Full time

Benefits offered by this job

Hybrid work arrangement

Job summary

TE Connectivity Ltd. seeks a Senior AI Data Product Owner to own end-to-end AI data products on AWS and Databricks. You will transform dashboards and datasets into governed, production-grade data products used for AI training, evaluation, inference, and monitoring.

The role advances TE Sensors toward an AI-first operating model, managing data assets as enterprise resources with clear ownership and lifecycle controls.

Qualifications

• Advanced degree not required if equivalent experience.
• Experience leading data product initiatives with measurable impact.
• Strong understanding of data governance, quality, and security.

Responsibilities

• Own AI data product roadmap with defined owners and SLAs.
• Define data contracts, schema, freshness, quality, and access rules.
• Lead ingestion and curation pipelines into AWS and Databricks.
• Establish governance, gates, and lineage for AI readiness.
• Provide production support and drive continuous improvement.

Skills

AI

AWS

Databricks

Metadata

Governance

Education

Bachelor's degree or higher in CS/DS/AI-ML/Engineering

Tools

Delta Lake

Unity Catalog

Job description

Role: Senior AI Data Product Owner

Location: Bangalore (Hybrid)

Budget: 30 LPA

Experience: 8-15 Years

Mandatory skills: AI,AWS, Databricks, Metadata, Governance

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.

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.