Zigsaw

Data Engineer

• 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

Staff Data Engineer

• Implement robust data infrastructure in AWS, using Spark with Scala
• Evolve our core data pipelines to efficiently scale for our massive growth
• Store data in optimal engines and formats
• Collaborate with our cross-functional teams to design data solutions that meet business needs
• Built out fault-tolerant batch and streaming pipelines
• Leverage and optimize AWS resources while designing for scale
• Collaborate closely with our Data Science and Product teams

Senior Data Engineer

Backend Engineer

• Own and deliver key product features from design through deployment, working closely with design, product, and other stakeholders.
• Mentor and support engineers, offering guidance and helping to reinforce high engineering standards across the team.
• Collaborate closely with design, product, and cross-functional stakeholders to deliver a user-friendly and engaging product for our customers.
• Ship high-performing and resilient APIs that adhere to industry standards and are delightful to use.
• Step into a technical lead role on projects as needed, coordinating across functions and guiding execution without formal authority.
• Maintain a high standard of quality across our codebase by advocating for cleanliness, readability, speed, testability, and documentation.
• Champion software quality, emphasizing readability, reliability, testability, and documentation.

Senior Backend Engineer

Machine Learning Engineer

• Write production Python that powers real-time bidding, model training, and campaign optimization
• Train, deploy, and monitor ML models that decide which ads to show, when, and at what price: millions of bid decisions per second
• Build and improve our incrementality measurement systems: helping advertisers understand the true causal lift of their CTV spend
• Design and implement new ML products across the ad-buying lifecycle: audience targeting, bid optimization, pacing, and attribution
• Use LLMs and generative AI to build internal tools that accelerate how we develop, test, and ship ML systems
• Serve as a technical lead and mentor on a distributed engineering team

Senior Machine Learning Engineer

Data Scientist

• Write production code in Python.
• Design, launch, and analyze experiments to optimize ad campaigns.
• Create reporting and analytics tools for the Data Science Team's customers.
• Translate insights from reporting tools into tvScientific's Data Science Product.

Senior Data Scientist

Full-stack Engineer

• Own end‑to‑end architecture and delivery of features for lift measurement products, from design and implementation through rollout and ongoing reliability.
• Design, build, and optimize large‑scale data pipelines that power study setup, experiment execution, and results computation.
• Drive AI enablement across the team by building the supporting tooling and processes (CI checks, test strategy, templates), coaching engineers, and ensuring adoption results in sustained improvements to delivery speed and system quality.
• Translate measurement methodology into production systems: partner with Data Science to operationalize study design requirements into pipelines, services, tooling, and guardrails.
• Build “rigor by default” mechanisms: automated data validation, randomization/holdout integrity checks, imbalance diagnostics, and continuous verification/backtesting.
• Raise the bar on data quality and trust: define SLAs, build monitoring and anomaly detection, and lead root‑cause analysis for pipeline and study issues.
• Mentor and up level engineers, driving strong technical execution and cross‑functional collaboration.

Staff Software Engineer, Ads Measurement Products

Technical Product Manager

• Partner with the VP of Infrastructure to set the multi-year InfraGov strategy and roadmap, aligning governance priorities to company goals and engineering/product plans.
• Partner with EPD TPM leadership to own annual and quarterly infrastructure budget planning and in-year re-forecasting; establish guardrails, decision rights, and escalation paths.
• Lead through a team of TPMs to deliver cross-org programs across spend control, efficiency, capacity management, and vendor management.
• Build executive partnership with Finance, Engineering, and Product to drive high-quality, data-informed trade-offs (innovation headroom vs. cost, risk, and capacity).
• Create governance mechanisms that scale: dashboards, source-of-truth reporting, business reviews, and accountability rhythms that drive consistent execution.
• Sponsor cloud vendor strategy and performance management in partnership with Procurement/Finance/Engineering, including renewal readiness and KPI oversight.
• Set the InfraGov TPM team strategy; hire, coach, and retain diverse leaders; set clear expectations and program craft standards; deliver calibrated feedback; create growth plans, rotations, and succession coverage across spend, efficiency, capacity, and vendor domains.

Director TPM, InfraGov

• Define and communicate a clear multi-year ML vision
• Identify and prioritize impactful Growth opportunities
• Establish success metrics for Growth ML systems
• Manage and develop managers and senior ML engineers
• Champion cross-functional initiatives that grow user visitation
• Define a unified ML architecture for Growth
• Act as primary ML point of contact for Growth with executives