Varos is a data sharing platform helping companies compare their KPIs to their peers. We recently participated in YC’s S21 batch and raised $4.2m Seed round from top-tier investors.

We're pioneering a new category of data analytics that’s challenging traditional taboos around cross-company collaboration. Varos’ platform helps its users understand their own performance vs. direct competitors by unlocking data that is currently stored in different silos. In fact, a growing base of hundreds of users are already automatically feeding marketing data to Varos’ platform, and in return seeing how they stack up against others in their space and stage. However, Varos is not planning to stop there, and is already planning its expansion across additional operative departments, such as finance, product, and sales.

Companies today are flying blind because they only rely on their own historical data and don’t know how their metrics compare to their competitors.

Founders includes the former CTO of Dun & Bradstreet ($15bn+ data company), an 8200 alum with 15 years experience building big data products and a former private equity investor.

We're looking for a data guru to build product features and run analyses on top of our massive data set (7,000+ companies first-party data co-op + large public database).

Opportunity to be first 10 employee in a very fast-growing startup that just raised from on of the best VCs in Silicon Valley.

You'll be working lots in SQL, DBT and Python + leveraging LLMs for data enrichment & tagging.

Requirements:

• 6+ years of analytics engineering, data engineering, data science, or other highly analytical, data-oriented roles.
• Excellent communication skills and ability to work with technical and non-technical partners from many teams, especially in exploring decisions and trade-offs
• Advanced proficiency in SQL databases and queries, as well as experience designing data models using dbt
• Working knowledge of Python and cloud-based data warehouse platforms (Snowflake and Athena in particular)
• Commitment to applying software engineering best practices in an analytics setting
• Experience building pipelines for Machine Learning or LLM models and ensuring their reliability using ML/LLM ops techniques
• Startup mentality, solution driven, can do approach

We have highly scalable data pipelines that process Terabytes of data every month, enrich data via integrations and enables zero latency queries all by maintaining data privacy. A large portion of the technical work is dealing with complex data normalization across companies and integrations, all in real-time.

Back-end mainly built with Python and SQL (using Snowflake and AWS). In the front-end, users gets a dashboard built in React.