Staff Software Engineer, DoubleVerify

• Deep expertise in SQL (window functions, CTEs, optimization)
• Strong Python skills with data libraries
• Proficiency with DuckDB (extensions, parquet/iceberg integration, embedding in pipelines)
• Hands-on with columnar formats (Parquet, Arrow, ORC) and schema evolution
• Expertise in Kubernetes and Helm
• Cloud storage experience (AWS S3, GCS)
• Experience with semantic layer frameworks (CubeJS)
• CI/CD tooling (GitHub Actions, Terraform, Docker/Kubernetes)
• Track record of leading architecture decisions and mentoring teams
• Ability to set standards for maintainability and developer experience
• Experience with serverless and embedded analytics (DuckDB WASM, in production)

Desirable

• We are looking for a Staff Data Engineer to shape the future of our data platform with a focus on small data at scale
• While many companies over-index on heavyweight distributed systems, we believe in the power of efficient, local-first, columnar engines like DuckDB to process and analyze data quickly, reliably, and cost-effectively
• As a Staff Data Engineer, you will set the technical direction for how our teams ingest, transform, and serve data, bridging the gap between lightweight embedded tools and cloud-scale systems
• You’ll be hands-on in building pipelines, while also mentoring engineers and setting best practices across the organization
• You'll design and implement data processing workflows using DuckDB, Polars, and Arrow/Parquet
• Balance small-data local pipelines with cloud data warehouse backends (Snowflake etc)
• Advocate for efficient, vectorized, local-first approaches where appropriate
• Drive best practices for designing reproducible and testable data workflows
• Partner with data science, professional services, and product engineering teams to define semantic data layers
• Provide technical leadership in how data is versioned, validated, and surfaced for downstream use
• Establish standards for CI/CD, observability, and reliability in data pipelines
• Automate workflows and optimize data layout for performance and cost efficiency
• Serve as a thought leader in the organization, guiding engineers on when to use lightweight tools vs. distributed platforms
• Mentor senior and mid-level data engineers to accelerate their growth
• What Success Looks Like:
• Data pipelines that are fast, simple, and reproducible—running in seconds or minutes, not hours
• A team that defaults to the right level of tooling for the problem (small-data-first, scale-up only when necessary)
• Clear semantic data definitions that power analytics, experimentation, and AI/ML initiatives
• Reduced infrastructure cost and complexity without sacrificing reliability