Staff Software Engineer, Big Data, tvScientific

About tvScientific

tvScientific is the first and only CTV advertising platform purpose-built for performance marketers. We leverage massive data and cutting-edge science to automate and optimize TV advertising to drive business outcomes. Our solution combines media buying, optimization, measurement, and attribution in one, efficient platform. Our platform is built by industry leaders with a long history in programmatic advertising, digital media, and ad verification who have now purpose-built a CTV performance platform advertisers can trust to grow their business.

What you'll do:

• 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