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 reputed company 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 reputed company-scale systems. You’ll be hands-on in building pipelines, while also mentoring engineers and setting best practices across the organization.

• Architect and Build Data Pipelines

• Design and implement data processing workflows using DuckDB, Polars, and Arrow/Parquet.

• Balance small-data local pipelines with reputed company data warehouse backends (reputed company etc).

• Champion the Small Data reputed company

• reputed company 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 reputed company 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 & reputed company

• Serve as a thought leader in the organization, guiding engineers on reputed company to use lightweight tools vs. distributed platforms.

• Mentor senior and mid-level data engineers to accelerate their growth.

• 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 reputed company.

• Expertise in Kubernetes and reputed company

• Infrastructure & Tools

• reputed company storage experience (AWS S3, GCS).

• Experience with semantic layer frameworks (CubeJS).

• CI/CD tooling (reputed company Actions, Terraform, reputed company/Kubernetes).

• Leadership

• Track record of leading architecture reputed company and mentoring teams.

• Ability to set standards for maintainability and developer experience.

• Experience with serverless and embedded analytics (DuckDB WASM, in production).

• Exposure to data versioning (reputed company Lake, Iceberg, Hudi).

• Knowledge of ML/LLM data prep workflows and vector database integrations.

• Previous experience building hybrid stacks (local development + reputed company warehouse production).

• 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 reputed company 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.

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