Senior Manager, Data Engineering
Role Overview
Lead the engineering organization for Nielsen's Databricks-based data and AI ecosystem, directly managing the Data Platform and AI/Data Engineering teams.
Unify strategy across GenAI, data engineering, platform governance, and FinOps, with accountability for people, delivery, budgets, and executive stakeholder engagement.
Key Skills for This Role
Full Job Posting
Strategic Mandate
Lead the engineering organization for Nielsen's Databricks-based data and AI ecosystem, directly managing the Data Platform and AI/Data Engineering teams.
Unify strategy across GenAI, data engineering, platform governance, and FinOps, with accountability for people, delivery, budgets, and executive stakeholder engagement.
Core Goals and Responsibilities
- Manage team hiring, mentoring, performance, career development, and planning.
- Set a unified engineering roadmap, OKRs, delivery cadence, and cross-team dependency management.
- Oversee Unity Catalog governance, security, reliability commitments, compliance, audit readiness, and governed AI enablement.
- Evaluate emerging Databricks and AI capabilities through proofs of concept and security and architecture reviews before adoption.
- Own Databricks budget tracking, FinOps governance, headcount planning, and vendor and licensing decisions.
- Engage senior leadership, Finance, HR, and Product stakeholders; foster shared standards and knowledge sharing.
Qualifications and Experience
- 10+ years in data or software engineering, including 3+ years directly managing engineering leads, managers, or senior individual contributors across distributed teams.
- Proven experience hiring, calibrating performance, developing careers, and building high-performing engineering teams.
- Experience with OKRs, roadmaps, capacity planning, cross-team dependencies, and cloud or platform budget ownership.
Expertise and Technology Stack
- Strong working knowledge of Databricks, Unity Catalog, Delta Lake, Delta Live Tables, Databricks Workflows, and Databricks SQL.
- Working understanding of GenAI application patterns, including LangChain, vector databases, and Databricks Genie.
- Able to evaluate architecture trade-offs and coach technical leads; day-to-day hands-on coding is not required.
- Able to synthesize technical trade-offs from multiple leads and influence decisions.
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