Build the AI-Ready Data Quality Platform
Define and ship native data quality capabilities inside Databricks Lakehouse
Productize policies and controls within Unity Catalog (lineage, access, schema enforcement)
Embed data contracts and validation logic directly into pipelines
Partner with data engineering to integrate dbt-based transformation layers into quality frameworks
Drive metadata, lineage, and semantic standardization as first-class platform features
Operationalize Data Quality in the AI Data Fabric
Design real-time anomaly detection systems (statistical + ML-driven)
Build upstream schema validation into CI/CD workflows (shift-left quality)
Define SLOs/SLAs for data products
Enable automated drift detection for training and inference datasets
Implement observability across streaming and batch architectures
Drive Data Ownership as a Product Discipline
Establish a data product ownership model across service teams
Define what “production-grade data” means for AI use cases
Build self-service tooling for teams to monitor and certify their data
Incentivize measurable quality accountability at the domain level
Define how governed datasets become AI-ready assets
Enable traceability from raw source → curated feature sets → model inputs
Align catalog metadata with AI feature stores and inference pipelines
Partner with ML teams to support model reproducibility and dataset versioning

Requirements

5+ years in Product Management for Data Platforms, Analytics, or AI Infrastructure
Deep working knowledge of: Databricks Lakehouse architecture
Unity Catalog governance constructs
dbt transformation workflows
CI/CD patterns for data pipelines
Data observability and monitoring patterns
Strong SQL fluency and comfort reading Python/Scala data pipeline code
Experience defining data contracts and schema evolution strategies
Understanding of streaming frameworks (Kafka, Spark Structured Streaming, etc.)
Experience supporting AI/ML workloads in production environments
Bonus: Experience with modern data observability platforms (Monte Carlo, Bigeye, etc.)
Familiarity with feature stores and model lifecycle tooling
Knowledge of domain-oriented data mesh architectures

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data qualitydata governanceSQLPythonScaladbtCI/CDanomaly detectiondata observabilitystreaming frameworks

Soft Skills

product managementdata ownershipcollaborationaccountabilitycommunication