Data Governance Engineer

About the Role

We are seeking a Data Governance & AI Trust Manager to define and operationalize the governance framework that enables high‑quality, well‑governed data to safely and effectively power GenAI agents, copilots, and analytics across the company.

As the organization accelerates adoption of Generative AI, the quality, structure, provenance, and clarity of corporate operating data have become critical enablers of AI performance, trustworthiness, and value. This role exists to ensure that the company’s operating data ecosystem is intentionally designed, consistently governed, and fit for AI‑enabled use cases, rather than fragmented, ambiguous, or ad‑hoc.

This role reports to the CISO & CIO and operates as a cross‑functional data governance authority across all company content. It does not own or manage the data itself; content ownership remains with business and platform owners. Instead, this role defines the standards, policies, and operating expectations under which data is created, managed, shared, retained, and made eligible for analytics and GenAI use.

A core responsibility of the role is to set the vision for the company’s internal data ecosystem—including how platforms such as SharePoint, Confluence, Jira, Slack, Teams, and related systems should be used together—and to establish clear operational standards (for example, when content belongs in SharePoint versus Confluence, and how that decision impacts AI use).

Governance is expected to be operationalized through native platform controls and existing enterprise tooling, rather than through standalone or heavyweight governance systems. The role translates governance principles into practical, enforceable standards that improve data quality, reduce ambiguity, and materially improve GenAI outcomes.

This is not a traditional data engineering or compliance analyst role. Success requires strong program ownership, the ability to drive cross‑functional alignment without direct authority, and sound judgment at the intersection of data quality, AI trust, security, and business operations.

Your Day‑to‑Day

Governance Framework Design & Ownership

• Own and evolve the company’s Corporate Operating Data Governance framework
• Define governance standards for unstructured and semi‑structured data across collaboration and productivity platforms
• Establish clear ownership models (Data Owners, Data Stewards, Custodians) for operating data domains
• Ensure governance practices enable business velocity rather than obstruct it
• Operationalize governance through native platform capabilities (e.g., metadata, permissions, retention, labeling, workflows) rather than standalone governance tooling

Metadata, Classification & AI Eligibility

• Define and maintain metadata standards, including:
  • Data classification
  • Purpose of use
  • Lifecycle state
  • AI eligibility and exclusion rules
• Partner with Security and Legal to ensure alignment with privacy, regulatory, and contractual obligations
• Embed metadata and classification standards directly into existing platforms rather than creating parallel systems

Data Quality & Trustworthiness

• Establish fitness‑for‑purpose data quality expectations for corporate operating data
• Define quality signals for:
  • Accuracy
  • Completeness
  • Timeliness
  • Consistency
• Partner with business teams to identify high‑risk or high‑impact data domains
• Ensure AI‑enabled experiences are grounded in authoritative, explainable, and traceable data sources

Lifecycle, Retention & Risk Management

• Define lifecycle expectations for operating data, including creation, maintenance, archival, and deletion
• Coordinate retention and defensibility standards to support legal, compliance, and audit needs
• Reduce long‑term risk from unmanaged or over‑retained unstructured data
• Support eDiscovery and investigation readiness through clarity and traceability

Cross‑Functional Execution & Enablement

• Drive accountability across Product, Engineering, Security, Legal, IT, and Business teams
• Establish practical governance cadences, reporting, and decision forums
• Provide authoritative guidance and operating standards while enabling teams with clear, pragmatic direction
• Foster a data‑aware and AI‑responsible culture through enablement and education rather than enforcement alone

What You Bring to the Team

• 6+ years of experience in data governance, data management, AI governance, security, or related operational leadership roles
• Experience governing unstructured or operational data (e.g., collaboration platforms, knowledge systems, document repositories)
• Strong understanding of data quality concepts and lifecycle management
• Familiarity with AI risk considerations including data provenance, bias, privacy, and misuse
• Demonstrated ability to drive cross‑functional execution without direct authority
• Excellent written and verbal communication skills for executive and technical audiences
• Sound judgment balancing governance rigor with business pragmatism
• High degree of ownership and follow‑through

What We’d Like to See (Not Required)

• Experience supporting AI governance, AI enablement, or responsible AI initiatives
• Exposure to regulated environments (HIPAA, HITRUST, SOC 2, GovRAMP, or similar)
• Familiarity with collaboration platforms such as Jira, Confluence, Slack, and Microsoft 365
• Experience partnering closely with Security, Legal, or Risk teams
• SaaS or data‑platform operating environment experience

What you’ll get in return 

• Competitive Leave & Benefits
• Comprehensive health coverage
• Equity for every employee – share in our success
• Growth-focused environment – your development matters here

Work arrangements 

• Standard hours: 8 hours/day, 5 days/week
• Location: Pune, Hybrid (3 days a week in office)
• Shift: 12pm-9pm IST