Senior Manager, Data Engineering

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.