Senior Manager, Data Engineering

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.

Employment type
Full-time

Work arrangement
Hybrid