Varos is an AI Market Research firm, providing competitive intelligence, benchmarks and market research.

We sell to both Marketing Executives and Tech Executives.

For Tech Executives: Cutting through the AI chaos, we deliver clear, actionable insights & market research that drive real ROI for enterprise tech leaders.

Our proprietary approach—blending expert interviews, robust data analysis, and deep vendor insights—cuts through the noise and delivers the customized guidance you need to make faster, smarter AI decisions.

Backed by top-tier venture capital and steered by a team of former enterprise CIOs, Varos Research transforms market intelligence into tangible success.

For Marketing Executives: We have built the largest first-party data co-op in digital marketing, offering highly granular and real-time benchmarks & market trends in a give-to-get model.

Get an instant McKinsey-level audit of your account, benchmarked against 7,000+ companies, including many similar to yours for your Meta/Google Ads.

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.