Senior Manager, Data Engineering

Thenielsencompany; Bengaluru; On-site; INR 400,000 - 700,000; Full time

Job summary

Nielsen is seeking an Engineering Manager to lead the AI/Data Engineering and Data Platform teams in Bengaluru. You will translate executive strategy into a unified roadmap for GenAI and data platforms, while owning people leadership, delivery accountability, and FinOps stewardship across the Databricks program.

You will engage senior stakeholders to balance priorities and ensure governance and reliability.

Qualifications

• 10+ years in data/software engineering with people leadership
• Experience managing engineering leads, managers or senior ICs
• Databricks/Data Platform fluency to guide architecture
• GenAI literacy to govern AI enablement

Responsibilities

• Lead AI/Data Engineering and Data Platform teams; hire, mentor, and manage performance
• Define a unified 1–roadmap for GenAI and data platforms with governance
• Set OKRs and cadence; own end-to-end delivery across teams
• Oversee governance (Unity Catalog, data security) and platform reliability (99.99%)
• Champion emerging Databricks/AI capabilities with a structured evaluation process
• Drive FinOps, budget planning and vendor/licensing decisions

Skills

Leadership

Databricks

GenAI

People Development

Program Mgmt

FinOps

Analytical Thinking

Tech Curiosity

Education

Engineering Leadership Experience

Job description

Strategic Mandate

As the Engineering Manager, you will lead and grow the engineering organization behind Nielsen's Databricks-based data and AI ecosystem - directly managing the Data Platform team and the AI/Data Engineering team. You are accountable for translating executive strategy into a single, unified roadmap that spans GenAI/data engineering and platform governance, ensuring these two disciplines operate in lockstep to drive shared business value. Beyond technical direction, you own people leadership, delivery accountability, and budget/FinOps stewardship for the combined Databricks program, and you are the primary point of escalation and executive stakeholder engagement when priorities, risks, or trade-offs span both teams

Core Goals & Responsibilities

• Org & People Leadership: Directly manage the AI/Data Engineering and Data Platform teams; own hiring, mentoring, performance management, career development, and career planning.
• Unified Technical Strategy: Shape a single, prioritized engineering plan that brings together GenAI and data-engineering priorities with platform, governance, and FinOps commitments.
• Delivery & Execution Accountability: Set OKRs, manage program/sprint cadences, and own end-to-end delivery accountability for the combined engineering organization - resolving cross-team dependencies, sequencing conflicts, and delivery risks.
• Governance & Risk Oversight: Provide senior oversight of Unity Catalog governance, data security, and platform reliability commitments (99.99%), ensuring org-wide compliance, audit readiness, and consistent governed AI enablement (e.g., Databricks Genie, AI/BI, etc).
• Emerging Technology Strategy: Partner with Strategy and Architecture to define standards and governance frameworks for emerging Databricks/AI capabilities, and run a structured evaluation process (POCs, security/architecture review, phased adoption) before they graduate into standard practice.
• Technology Trend Championing: Stay technologically upfront - track industry and Databricks/AI trends, personally trial promising capabilities, and champion adoption of what fits, balanced against a prioritized backlog and delivery commitments.
• FinOps & Budget Ownership: Own the Databricks budget planning & tracking, finops governance, headcount planning, and vendor/licensing decisions, bringing in cost-efficiency accountability and "Strategic Foresight" targets on cloud spend.
• Executive & Cross-Functional Stakeholder Management: Serve as the primary interface between the engineering organization and senior leadership, Finance, HR, and Product stakeholders - translating business priorities into technical direction and reporting progress upward.
• Culture & Community: Champion a unified community of practice across data engineering and platform engineering, ensuring consistent engineering standards, mentorship, and knowledge sharing across both teams.

Expertise & Technology Stack

• Leadership Experience: 10+ years in data/software engineering, including 3+ years directly managing engineering leads, managers, or senior ICs across distributed teams.
• Databricks & Data Platform Fluency: Strong working knowledge of the Databricks ecosystem (Unity Catalog, Delta Lake, Delta Live Tables, Databricks Workflows, Databricks SQL) sufficient to evaluate architecture trade-offs and coach technical leads, without requiring day-to-day hands-on coding.
• AI/GenAI Literacy: Working understanding of GenAI application patterns (LangChain, vector databases, Databricks Genie, etc) to guide governed AI enablement decisions across both teams.
• People & Org Development: Proven track record of hiring, performance calibration, career pathing, and building high-performing, retention-focused engineering teams.
• Program & Delivery Management: Experience running OKRs/roadmaps, capacity planning, and cross-team dependency management across multiple engineering disciplines.
• FinOps & Budget Management: Experience owning cloud/platform budgets and driving cost-efficiency accountability across teams.
• Analytical & Influencing Skills: Ability to synthesize technical trade-offs surfaced by multiple leads into a single executive narrative, and to influence decisions without dictating implementation details.
• Technology Curiosity: A demonstrated habit of tracking industry and Databricks/AI trends, hands-on experimentation to validate fit, and driving pragmatic adoption rather than chasing hype.

Requirements & Qualifications

• Experience: 10+ years in data/software engineering, with 3+ years in an Engineering Manager or People Manager role overseeing senior/lead engineers delivering large-scale data or AI platforms.
• Education/Certification: Required: Proven track record of managing engineering leads responsible for large-scale, governed data and AI platform delivery.
• Education/Certification: Preferred: Databricks Certified Data Engineer Professional / Platform Architect, or a formal people-leadership/management certification.
• Communication: A "Bottom Line Up Front" (BLUF) communicati