Data Platform and Agentic Governance Lead, HR Technology

Meta; Austin (TX); On-site; USD 151,000 - 213,000; Full time

Job summary

Meta is hiring a Data Platform & Governance Lead for HR Tech Data & Insights. You will own the Databricks architecture, the semantic layer, and governance standards for People data, ensuring policy-driven access and trusted data products across HR domains.

You will drive the migration from legacy stacks to governed Databricks data products, mentor teams, and collaborate with Directors and VPs to frame technical tradeoffs in business terms.

Qualifications

• Strong data governance experience with sensitive data.
• Deep hands-on Databricks expertise incl. Unity Catalog and Delta Lake.
• Ability to present technical strategy to executive stakeholders.

Responsibilities

• Own the technical layer of the People Operations Data strategy.
• Provide governance standards and oversee Databricks implementation for people data.
• Design catalog, schema, ACLs, row filters, and masking for protected data.
• Define semantic layer and certified datasets across People Ops.
• Lead migration from legacy Hive/Presto to governed Databricks data products.

Skills

Databricks

Spark

Governance

SQL

Python

Data Platform

Executive Stakeholders

Migration

Education

Bachelor's in CS

Tools

Unity Catalog

Delta Lake

Databricks SQL

Spark

Airflow

Job 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.Data Platform and Agentic Governance Lead, HR Technology 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 LakehouseProvide technical oversight of the Databricks implementation for people data — ingestion patterns from Workday, HR service operations, and vendor feedsDelta modeling and medallion layeringSpark declarative pipelinesand materialization strategy. You set the patternsyou do not have to write every pipelineOwn 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 conventionOwn 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 truthSet 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 controlsManage Agent & Data platform integrity: drive ontology-driven extraction, semantic retrieval, and governed metadata that AI assistants can trustOwn 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 auditorRepresent 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 credibilityLead the migration off the legacy stack. Sequence consolidation off Hive/Presto and bespoke pipelines onto governed Databricks data products, without breaking consumers mid-flightDrive prioritization and execution across partner teams outside your direct reporting line, translating business outcomes into a sequenced technical roadmapOwn 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 stakeholdersMake 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 humanBuild 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 teamsExercise conservative judgment with sensitive and privileged people data — including access-control and privilege-level, privacy, and data-classification considerations — and identify risk in others' designsMinimum Qualifications:Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience12+ years of experience in data platform, data architecture, data governance, or data engineeringDeep hands-on experience and Databricks certifications — deep hands-on depth across Unity Catalog, Delta Lake, Spark declarative pipelines, and Databricks SQLApache Spark expertise, including performance diagnosis and optimization in production, including performance diagnosis and optimization in productionExperience owning a data platform or data governance domain end to end at enterprise scale — you set the standards others followedHands-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 toolingDemonstrated experience defining a semantic or metrics layer and driving adoption of certified definitions across an organizationProven experience designing fine-grained data access controls (table ACLs, row-level filters, column masking) for confidential or regulated dataAdvanced SQL proficiency and working Python experience sufficient to build and debug data pipelinesExperience influencing senior leadership on technical strategy, including presenting to and driving decisions with executive stakeholdersExperience driving execution across cross-functional teams outside of direct reporting linesExperience defining measurable milestones and communicating delivery progress and business impact to non-technical stakeholdersExperience leading a platform migration or consolidation with live stakeholders and no acceptable downtimeDemonstrated judgment handling highly confidential, privileged, or regulated dataPreferred Qualifications: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 requirementsExperience 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 technologiesExperience with dbt, Airflow, or similar transformation and orchestration toolingDemonstrated track record mentoring and growing technical talent across experience levelsDemonstrated 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 partiesExperience 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 itExperience building the governed data and evaluation foundations that AI or LLM-based tools depend on, including accuracy measurementExperience with FinOps and cost attribution for cloud data platformsExperience with streaming and near-real-time ingestion (Kafka, Flink, Structured Streaming, CDC) and knowing when *not* to use itExperience with config-driven data onboarding platforms and infrastructure-as-code patternsExperience operating amid ambiguity and in-flight roadmaps, converting uncertainty into decision frameworksAbout Meta:Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.Meta is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Meta participates in the E-Verify program in certain locations, as required by law. Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment.Meta is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at [e-mail].$151,000/year to $213,000/year + bonus + equity + benefitsIndividual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about benefits at Meta.