You will work at the intersection of data engineering, identity resolution, privacy, and platform reliability. This is an individual contributor role, where you will work to define and implement a strategic vision for data engineering within the organization.

Responsibilities:

• Design and maintain a scalable identity resolution platform
• Build pipelines and services to ingest, normalize, link, and version identity data across multiple sources
• Ensure deterministic and probabilistic matching logic that is transparent, auditable, and measurable
• Partner with product and analytics teams to expose identity data through reliable, well-documented APIs and datasets
• Build and operate batch and streaming pipelines using modern data stack tools
• Create clear documentation, standards, and runbooks for identity and governance systems
• Own data governance foundations including data lineage, quality checks, schema enforcement, and access controls
• Implement privacy-by-design principles (PII handling, consent enforcement, retention policies)
• Collaborate with legal, privacy, and security teams to operationalize regulatory requirements (e.g., GDPR, CCPA)
• Establish monitoring and alerting for data quality, freshness, and integrity

What we're looking for:

• Production data engineering experience
• Bachelor’s degree in computer science, related field or equivalent experience
• Proficiency in Spark and Scala, with proven experience building data infrastructure in Spark using Scala
• Experience in delivering significant technical initiatives and building reliable, large scale services
• Experience in delivering APIs backed by relationship-heavy datasets
• Experience implementing data governance practices, including data quality, metadata management, and access controls
• Strong understanding of privacy-by-design principles and handling of sensitive or regulated data
• Familiarity with data lakes, cloud warehouses, and storage formats
• Strong proficiency in AWS services
• Excellent written and verbal communication skills
• Successful design and implementation of scalable and efficient data infrastructure
• High attention to detail in implementation of automated data quality checks
• Effective collaboration with cross-functional teams
• Demonstrated ability to use AI to improve speed and quality in your day-to-day workflow for relevant outputs
• Strong track record of critical evaluation and verification of AI-assisted work (e.g., testing, source-checking, data validation, peer review)
• High integrity and ownership: you protect sensitive data, avoid over-reliance on AI, and remain accountable for final decisions and deliverables

XML job scraping automation by YubHub