Copy Of Lead Databricks Engineer Senior Software Engineer II

At Nielsen, we are passionate about our work to power a better media future for all people by providing powerful insights that drive client decisions and deliver extraordinary results. Our talented, global workforce is dedicated to capturing audience engagement with content - wherever and whenever it’s consumed. Together, we are proudly rooted in our deep legacy as we stand at the forefront of the media revolution. When you join Nielsen, you will join a dynamic team committed to excellence, perseverance, and the ambition to make an impact together. We champion you, because when you succeed, we do too. We enable your best to power our future.

Strategic Mandate

As the Data Platform Lead, you are the technical owner and steward of Nielsen's Databricks Lakehouse for Corporate function. You will drive the strategy, architecture, and operational maturity of our Databricks ecosystem end to end — from workspace and Unity Catalog design to cost governance and platform reliability. This role sits at the intersection of platform engineering and data governance: you will define the guardrails, standards, and reusable patterns that let every engineering and analytics team build confidently on a secure, scalable, and cost-efficient foundation. You will act as the "Platform Authority" for Databricks, ensuring the lakehouse scales with the business while remaining governed, observable, and audit-ready. Critically, you will build an AI-driven data platform — architecting AI-native capabilities (feature stores, vector search, GenAI-ready pipelines) directly into the platform, while also using AI-assisted and agentic engineering practices to build, manage, and scale the platform itself faster and more reliably

Core Goals & Responsibilities

• Lakehouse Platform Ownership: Own the end-to-end architecture of the Databricks Lakehouse, including workspace topology, cluster policies, compute strategy (job clusters, SQL warehouses, serverless), and multi-cloud deployment patterns (AWS/Azure/GCP).
• Unity Catalog & Governance: Design and enforce the global data governance model — catalogs, schemas, access controls, lineage, and audit logging — via Unity Catalog, ensuring consistent metadata management and fine-grained access across business units.
• Data Engineering Standards: Define and enforce best practices for Delta Lake table design, Delta Live Tables (DLT) pipelines, medallion architecture (bronze/silver/gold), and performance optimization (Z-ordering, liquid clustering, partitioning, file compaction).
• Platform Reliability & Automation: Partner with CloudOps and DevOps to industrialize the platform through Infrastructure-as-Code (Terraform/Databricks Asset Bundles), CI/CD pipelines, and monitoring, targeting 99.99% platform availability.
• FinOps & Cost Governance: Own cost transparency and optimization across the Databricks estate — cluster right-sizing, workload isolation, chargeback/showback models, and proactive budget alerting — bringing "Strategic Foresight" to cloud spend.
• Cross-Functional Enablement: Work directly with Data Engineering, Analytics, Data Science, and business stakeholders (Finance, HR, Product) to translate requirements into platform capabilities and self-service patterns.
• Governed AI Enablement: Own the governed rollout of AI use cases and native AI features (e.g., Databricks Genie, AI/BI Dashboards, Mosaic AI) on the platform. You hold the keys to unlocking GenAI and natural-language analytics for the business, safely, within Unity Catalog's governance and access-control boundaries.
• Community & Mentorship: Build and lead a community of practice for Databricks developers across the organization — publishing standards, running enablement sessions, and unblocking delivery friction for engineering teams

Expertise & Technology Stack

• Core Engineering: 8–10+ years of deep experience in data engineering, data platform architecture, or distributed systems, with 3+ years specifically architecting and operating Databricks environments at scale.
• Databricks Platform Engineering: Proven hands-on experience building, managing, running, and scaling a production-grade Databricks Data Platform end-to-end — including Unity Catalog, Delta Lake, Delta Live Tables, Databricks Workflows, Databricks SQL, and cluster/compute policy design.
• AI-Native Platform & AI-Assisted Engineering: Experience architecting AI-native platform capabilities (feature stores, vector search/indexes, GenAI-ready pipelines) into the platform, combined with using AI-assisted and agentic engineering practices (e.g., AI coding copilots, automated testing/ops agents) to build, manage, and scale the platform itself.
• Distributed Compute: Deep hands-on expertise with Apache Spark (batch and structured streaming) for large-scale data processing and performance tuning.
• Frameworks & Languages: Expert-level proficiency in Python and SQL, with working knowledge of dbt for transformation-layer standardization.
• Infrastructure & Automation: Proven experience with Terraform (or Databricks Asset Bundles) and CI/CD tooling to codify and automate platform provisioning and pipeline deployment.
• Security & Governance: Strong grasp of data security, RBAC/ABAC, PII handling, and compliance requirements within a governed lakehouse.
• Analytical Thinking: Ability to challenge status-quo assumptions, evaluate architectural trade-offs, and provide a pragmatic pivot when technical or cost risks arise

Requirements & Qualifications

• Experience: 8–10+ years in a Senior or Lead Data Engineering / Data Platform capacity, including direct ownership of a production Databricks environment.
• Education/Certification:
• Required: Proven track record of building, managing, running, and scaling large-scale, multi-tenant Databricks Data Platforms.
• Preferred: Databricks Certified Data Engineer Professional or Databricks Certified Platform Architect.
• Communication: A "Bottom Line Up Front" (BLUF) communication style. You must be able to engage with both technical teams and non-technical stakeholders (HR/Finance) and clearly explain the "how" and "why" behind platform decisions

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