Data Platform and Agentic Governance Lead, HR Technology

Description

HR Tech Data & Insights builds the governed data foundation that Meta's People organization runs on — the certified datasets, semantic models, and data products that People Ops, People@, Payroll, business partners, and AI agents rely on to answer questions and make decisions. Meta is converging its enterprise data onto the Databricks Lakehouse, and people data is one of the most consequential and most constrained workloads in that program. We are hiring a Data Platform & Governance Lead to own the technical layer of our People Data strategy: the Databricks architecture, the semantic layer, the governance standards, and the paved road every other team builds on. This is a senior technical ownership and influence role — the ideal candidate pairs deep Databricks and Spark expertise with demonstrated governance experience that sensitive people data demands, and can present a technical strategy to executive stakeholders and diagnose query performance issues with equal depth.

Responsibilities

Own the technical layer of the People Operations Data strategy. Set and enforce the platform, semantic, and governance standards that every HR Tech and People@ data domain is built on in the Databricks Lakehouse Provide technical oversight of the Databricks implementation for people data — ingestion patterns from Workday, HR service operations, and vendor feeds; Delta modeling and medallion layering; Spark declarative pipelines; and materialization strategy. You set the patterns; you do not have to write every pipeline Own governance in Unity Catalog. Design catalog and schema structure, table ACLs, row filters, and column masks so sensitive people data is protected by policy as code in the data layer rather than by convention Own the semantic layer & Knowledge graph. Define certified datasets, actively develop knowledge graph nodes, metric definitions, and governed data products so that Immigration, payroll and ops numbers mean one thing across People Ops — and so analysts, dashboards, and AI agents all resolve to the same source of truth Set and enforce data governance standards: metadata and documentation, glossary and metric coverage, data ownership and pipeline SLAs, schema and naming conventions, certification tiers, and access controls Manage Agent & Data platform integrity: drive ontology-driven extraction, semantic retrieval, and governed metadata that AI assistants can trust Own data quality, process monitoring and observability — monitoring, tiered alerting, lineage and impact analysis, data-quality expectations, and automated remediation. Treat lineage as the compliance record we can show an auditor Represent the strategy with senior leaders. Frame technical tradeoffs in business terms in forums with Director and VP stakeholders across People Ops, Enterprise Engineering, Legal, and Privacy — driving decisions and building peer-level credibility Lead the migration off the legacy stack. Sequence consolidation off Hive/Presto and bespoke pipelines onto governed Databricks data products, without breaking consumers mid-flight Drive prioritization and execution across partner teams outside your direct reporting line, translating business outcomes into a sequenced technical roadmap Own a live, cross-domain view of delivery — milestones, owners, dependencies, status — and connect it to an evidence-backed narrative of business impact for People Ops stakeholders Make AI on people ops data trustworthy. Ensure the accuracy and effectiveness of AI agents and analytics tools built on people data, including evaluation datasets, accuracy measurement, and sustained quality benchmarks — with identity propagated end to end so agents run as the human Build capability and raise the bar. Mentor analytics and engineering partners on platform and governance fundamentals, lead design reviews and architecture decision records, and partner closely with the core data engineering and Databricks platform teams Exercise conservative judgment with sensitive and privileged people data — including access-control and privilege-level, privacy, and data-classification considerations — and identify risk in others' designs

Qualifications

Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience 12+ years of experience in data platform, data architecture, data governance, or data engineering Deep hands-on experience and Databricks certifications — deep hands-on depth across Unity Catalog, Delta Lake, Spark declarative pipelines, and Databricks SQL Apache Spark expertise, including performance diagnosis and optimization in production, including performance diagnosis and optimization in production Experience owning a data platform or data governance domain end to end at enterprise scale — you set the standards others followed Hands-on experience with modern data stacks: distributed warehouses and lakehouse architectures, pipeline orchestration and reliability, semantic/metrics layers, BI and self-service platforms, and data quality tooling Demonstrated experience defining a semantic or metrics layer and driving adoption of certified definitions across an organization Proven experience designing fine-grained data access controls (table ACLs, row-level filters, column masking) for confidential or regulated data Advanced SQL proficiency and working Python experience sufficient to build and debug data pipelines Experience influencing senior leadership on technical strategy, including presenting to and driving decisions with executive stakeholders Experience driving execution across cross-functional teams outside of direct reporting lines Experience defining measurable milestones and communicating delivery progress and business impact to non-technical stakeholders Experience leading a platform migration or consolidation with live stakeholders and no acceptable downtime Demonstrated judgment handling highly confidential, privileged, or regulated data Experience with HR/people data domains and their systems of record — Workday, Immigration, payroll, benefits, equity, HR case management — or a comparable regulated environment with audit and controls requirements Experience integrating enterprise third-party systems (HCM/payroll, ERP, vendor data feeds) Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Experience with dbt, Airflow, or similar transformation and orchestration tooling Demonstrated track record mentoring and growing technical talent across experience levels Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Experience with Delta Sharing or equivalent zero-copy sharing to avoid ETL and file handoffs with third parties Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Familiarity with Meta's internal data stack (Hive, Presto, real-time analytics tools, Configerator, workflow orchestration tools) and the work of migrating off it Experience building the governed data and evaluation foundations that AI or LLM-based tools depend on, including accuracy measurement Experience with FinOps and cost attribution for cloud data platforms Experience with streaming and near-real-time ingestion (Kafka, Flink, Structured Streaming, CDC) and knowing when *not* to use it Experience with config-driven data onboarding platforms and infrastructure-as-code patterns Experience operating amid ambiguity and in-flight roadmaps, converting uncertainty into decision frameworks

Compensation: $151,000/year to $213,000/year + bonus + equity + benefits