Principal Data Engineer (R-19440)

We are seeking an experienced Principal Data Engineer to help build the next generation of our identity graph and data platform. This role is focused on designing, developing, and optimizing large-scale data pipelines and systems that ingest, process, and unify complex datasets from diverse sources (web, mobile, AdTech, government, and proprietary data). This is a highly hands-on, technical role for someone who can quickly understand existing systems, operate independently, and deliver high-quality solutions at scale. The ideal candidate is deeply analytical, detail-oriented, and experienced with building performant data pipelines and systems handling billions of records.

• Design, build, and optimize scalable data pipelines and ETL/ELT workflows for large, complex datasets.
• Design and implement foundational data architecture supporting identity resolution and ID graph systems.
• Develop and enhance systems supporting identity resolution and ID graph construction (data ingestion, normalization, matching, and deduplication).
• Process and unify multi-source datasets (cookies, device IDs, behavioral data, third-party and proprietary data).
• Write efficient, testable, and maintainable code using Python and SQL for large-scale data processing.
• Optimize data models, queries, and storage strategies for performance, scalability, and cost efficiency.
• Build and maintain data validation, monitoring, and alerting systems to ensure data quality and reliability.
• Troubleshoot, debug, and improve existing data pipelines and infrastructure.
• Own and drive complex data problems end-to-end, from initial design through production deployment.
• Make and influence key technical decisions related to data architecture, scalability, and system design.
• Collaborate with data, platform, DevOps, and product teams to deliver scalable, production-ready solutions.
• Translate business and product requirements into practical, performant data solutions.
• Document data pipelines, systems, and workflows clearly.
• Continuously improve system performance, data quality, and pipeline resilience.
• Contribute to building new capabilities that improve how customers understand and leverage data insights

• 8-12+ years of hands-on experience in data engineering or large-scale data processing.
• Proven experience building and maintaining production-grade data pipelines and distributed systems.
• Demonstrated experience architecting and delivering large-scale data platforms or mission-critical data systems.
• Strong expertise in: SQL and relational databases (Postgres, BigQuery, Redshift, etc.), Python for data processing and analysis.
• Experience with Google Cloud Platform (BigQuery, Dataflow, Pub/Sub, Cloud Storage, Cloud Functions) and/or AWS (S3, Redshift, EMR, RDS).
• Experience working with large-scale datasets (hundreds of millions to billions of records).
• Strong understanding of data modeling, partitioning, indexing, and query optimization.
• Experience with distributed data processing and parallelization techniques.
• Experience moving large volumes of data across systems and architectures.
• Familiarity with CI/CD, containerization, and orchestration tools (Docker, Kubernetes, GitHub Actions, etc.).
• Strong debugging and troubleshooting skills in complex data environments.
• Experience with version control (Git) and Agile tools (Jira, Confluence, etc.).
• Highly analytical with strong attention to detail and a data-driven mindset.
• Ability to hit the ground running, quickly understand systems, and deliver independently.
• Comfortable working in a remote, fast-paced, and collaborative environment.
• Proven ability to drive system design and implementation.

Preferred:

• Experience with identity graphs, entity resolution, or record linkage systems.
• Background in AdTech, digital identity, cookies, or audience data platforms.
• Experience with real-time or streaming data systems.
• Familiarity with data quality, observability, and monitoring frameworks.
• Experience with data visualization tools (Looker, Tableau, Power BI).
• Knowledge of data privacy, compliance, and governance considerations.
• Experience with modern data platforms such as Snowflake and Databricks.
• Exposure to AI/ML technologies, including experience working with or integrating agentic frameworks.

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