Staff Software Engineer, Big Data, Tvscientific

Pinterest Pinterest · Consumer · San Francisco, CA · tvScientific

Staff Data Engineer to lead the design, implementation, and evolution of identity services and a data governance platform, focusing on privacy-safe and well-governed data. The role involves building pipelines, ensuring matching logic, exposing data via APIs, and implementing privacy-by-design principles and regulatory compliance.

What you'd actually do

  1. Design and maintain a scalable identity resolution platform
  2. Build pipelines and services to ingest, normalize, link, and version identity data across multiple sources
  3. Ensure deterministic and probabilistic matching logic that is transparent, auditable, and measurable
  4. Partner with product and analytics teams to expose identity data through reliable, well-documented APIs and datasets
  5. Build and operate batch and streaming pipelines using modern data stack tools

Skills

Required

  • Production data engineering 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

Nice to have

  • Bachelor’s degree in computer science, related field or equivalent experience
  • 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)

What the JD emphasized

  • Strong understanding of privacy-by-design principles and handling of sensitive or regulated data
  • Collaborate with legal, privacy, and security teams to operationalize regulatory requirements (e.g., GDPR, CCPA)
  • High integrity and ownership: you protect sensitive data, avoid over-reliance on AI, and remain accountable for final decisions and deliverables