ID.me is looking to solve the problem of efficiently and accurately acquiring, validating, and structuring data to power its identity verification and fraud prevention systems, ensuring data flows are accurate, compliant, and scalable across its network.
Requirements
- 10+ years of product management experience, with at least 5+ years focused on data platforms, ingestion, or infrastructure products.
- Demonstrated success delivering large-scale data pipelines or normalization systems in a high-growth or regulated environment.
- Deep technical understanding of ETL/ELT architectures, data lakes, and real-time streaming frameworks (e.g., Kafka, Spark, Airflow).
- Experience defining and managing data schemas, contracts, and version control at scale.
- Familiarity with data governance frameworks, metadata management, and cataloging tools.
- Exposure to identity, fintech, or fraud detection systems where data provenance and accuracy are critical.
- Strong understanding of compliance requirements (NIST 800-63, HIPAA, FedRAMP, GDPR).
Responsibilities
- Define and drive the product vision for how identity and verification data are acquired, normalized, and governed across ID.me's platform.
- Lead roadmap development and delivery for data acquisition pipelines, normalization workflows, and schema versioning systems.
- Collaborate with Engineering to design scalable ingestion and transformation architectures that ensure high data quality and availability.
- Define data validation and enrichment standards to maintain consistency across ID.me's Identity Trust Graph and associated domain graphs.
- Partner with the Fraud Analytics, Compliance, and Platform teams to ensure data integrity supports identity verification, risk assessment, and audit requirements.
- Establish data lineage and audit frameworks for compliance with NIST 800-63, HIPAA, FedRAMP, and GDPR.
- Implement metadata and cataloging solutions to enhance discoverability, transparency, and governance.
Other
- required 5 days/week in office
- Act as the connective tissue between Data Engineering, Platform, Fraud, and Analytics teams to align data priorities and drive end-to-end execution.
- Collaborate with the Platform organization to ensure normalized data integrates seamlessly into APIs, events, and downstream analytics.
- Influence architectural decisions and product trade-offs that balance accuracy, performance, and compliance.
- Partner with Security and Legal to ensure all data ingestion and sharing practices adhere to regulatory and privacy standards.
