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About the Role

HR Tech Data & Insights builds the governed data foundation for Meta's People organization, providing certified datasets, semantic models, and data products for decision-making. This role will own the technical layer of the People Data strategy, focusing on Databricks architecture, semantic layer, governance standards, and establishing a robust data platform.

Responsibilities

Own the technical layer of the People Operations Data strategy, setting and enforcing platform, semantic, and governance standards for the Databricks Lakehouse.
Provide technical oversight of the Databricks implementation for people data, including ingestion patterns, Delta modeling, Spark pipelines, and materialization strategy.
Own governance in Unity Catalog, designing catalog and schema structure, and access controls for sensitive people data.
Own the semantic layer & Knowledge graph, defining certified datasets, knowledge graph nodes, metric definitions, and governed data products.
Set and enforce data governance standards, including metadata, glossary, ownership, SLAs, naming conventions, certification, and access controls.
Manage Agent & Data platform integrity by driving ontology-driven extraction, semantic retrieval, and governed metadata for AI assistants.
Own data quality, process monitoring, and observability, including monitoring, alerting, lineage, impact analysis, data-quality expectations, and automated remediation.
Represent the strategy with senior leaders, framing technical tradeoffs in business terms and driving decisions with executive stakeholders.
Lead the migration off the legacy stack, consolidating from Hive/Presto and bespoke pipelines onto governed Databricks data products.
Drive prioritization and execution across partner teams, translating business outcomes into a technical roadmap.
Own a cross-domain view of delivery, connecting milestones, owners, and dependencies to business impact.
Ensure the accuracy and effectiveness of AI agents and analytics tools built on people data.
Build capability and raise the bar by mentoring partners, leading design reviews, and partnering with core data engineering and Databricks platform teams.
Exercise conservative judgment with sensitive and privileged people data, including access-control, privacy, and data-classification considerations.

Requirements

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, including Unity Catalog, Delta Lake, Spark declarative pipelines, and Databricks SQL.
Apache Spark expertise, including production performance diagnosis and optimization.
Experience owning a data platform or data governance domain end to end at enterprise scale.
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 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.

Skills

Databricks
Spark
Unity Catalog
Delta Lake
Spark declarative pipelines
Databricks SQL
Apache Spark
Modern data stacks
Distributed warehouses
Lakehouse architectures
Pipeline orchestration
Reliability
Semantic layers
Metrics layers
BI platforms
Self-service platforms
Data quality tooling
SQL
Python
Data access controls
Table ACLs
Row-level filters
Column masking
Metadata
Documentation
Glossary
Data ownership
Pipeline SLAs
Schema conventions
Naming conventions
Certification tiers
Access controls
Ontology-driven extraction
Semantic retrieval
Governed metadata
Data quality
Process monitoring
Observability
Lineage analysis
Impact analysis
Automated remediation
AI agents
Analytics tools
Evaluation datasets
Accuracy measurement
Quality benchmarks
Identity propagation
Design reviews
Architecture decision records
Data classification
dbt
Airflow
Delta Sharing
Responsible AI
Ethical AI
Risk assessment
Bias mitigation
Meta's internal data stack
Hive
Presto
Real-time analytics tools
Configerator
Workflow orchestration tools
FinOps
Cost attribution
Streaming ingestion
Near-real-time ingestion
Kafka
Flink
Structured Streaming
CDC
Config-driven data onboarding platforms
Infrastructure-as-code patterns
Prompt engineering
Context engineering
Agent orchestration

Location

Remote

Work Type

Full-time

Experience Level

Senior
12+ years of experience

Education Level

Bachelor's degree in Computer Science, Computer Engineering, or a relevant technical field

Salary/Compensations

$151,000/year to $213,000/year

Benefits

bonus
equity
benefits

About the Company

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.