About the Role:

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.

 

Responsibilities:

• Architect and Build Data Pipelines
  
  
• Design and implement data processing workflows using DuckDB, Polars, and Arrow/Parquet.
• Balance small-data local pipelines with cloud data warehouse backends (Snowflake etc).
  
  
• Champion the Small Data Mindset
  
  
• Advocate for efficient, vectorized, local-first approaches where appropriate.
• Drive best practices for designing reproducible and testable data workflows.
  
  
• Collaborate Cross-Functionally
  
  
• 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.
  
  
• Operational Excellence
  
  
• Establish standards for CI/CD, observability, and reliability in data pipelines.
• Automate workflows and optimize data layout for performance and cost efficiency.
  
  
• Mentor & Lead
  
  
• 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.
  
  

Qualifications:

• Core Technical Skills
  
  
• 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
  
  
• Infrastructure & Tools
  
  
• Cloud storage experience (AWS S3, GCS).
• Experience with semantic layer frameworks (CubeJS).
• CI/CD tooling (GitHub Actions, Terraform, Docker/Kubernetes).
  
  
• Leadership
  
  
• Track record of leading architecture decisions and mentoring teams.
• Ability to set standards for maintainability and developer experience.
  
  

Nice to Have:

• Experience with serverless and embedded analytics (DuckDB WASM, in production).
• Exposure to data versioning (Delta Lake, Iceberg, Hudi).
• Knowledge of ML/LLM data prep workflows and vector database integrations.
  
  
• Previous experience building hybrid stacks (local development + cloud warehouse production).
  
  

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.

The successful candidate’s starting salary will be determined based on a number of non-discriminating factors, including qualifications for the role, level, skills, experience, location, and balancing internal equity relative to peers at DV.
The estimated salary range for this role based on the qualifications set forth in the job description is between [$128,000 - $230,000]. This role will also be eligible for bonus/commission (as applicable), equity, and benefits.
The range above is for the expectations as laid out in the job description; however, we are often open to a wide variety of profiles, and recognize that the person we hire may be more or less experienced than this job description as posted.

Not-so-fun fact: Research shows that while men apply to jobs when they meet an average of 60% of job criteria, women and other marginalized groups tend to only apply when they check every box. So if you think you have what it takes but you’re not sure that you check every box, apply anyway!