Staff Software Engineer (Data) - Rockerbox

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 relat