Analytics Engineer

Reposted Yesterday
San Francisco, CA, USA
In-Office
160K-250K Annually
Entry level
Artificial Intelligence • Software
Building embeddings-based search infrastructure
The Role
Build and maintain canonical datasets, scalable ETL/ELT pipelines, internal data products, self-service analytics tools, and BI infrastructure. Analyze petabytes of web search data, optimize data systems for performance and cost, develop forecasting and experimentation models, and own A/B testing infrastructure. Partner cross-functionally to establish accurate metrics and support data-driven decisions across product, finance, marketing, and go-to-market teams.
Summary Generated by Built In

Exa is an applied AI lab building a search engine unlike the world has ever seen. We build massive-scale infra to crawl the entire web, train state-of-the-art embedding models to process it, and design super high performant vector databases to retrieve over it. We now power search for Cursor, Cognition, HubSpot, and over 400,000 developers and have raised $350m from Lightspeed, Benchmark, and a16z.

Our ultimate goal is to build perfect search over all the world's information, far beyond Google. If you want to build massive-scale ML systems that will define the way the new AI world consumes information, this is the place for you.

 

This role sits at the intersection of analytics, data science, and data engineering. As an Analytics Engineer, you’ll build internal data products that influence decision-making across the whole company. You'll have enormous autonomy in leading analytics projects, as well as designing and maintaining the data pipelines that power them.

Who you are

  • Strong proficiency in Python, SQL, and cloud services for data processing, warehousing, and orchestration.

  • Obsessively detail-oriented over establishing accurate metrics with analytical rigor.

  • A proactive cross-functional champion for data-driven decision-making, data integrity, and rigorous data requirements grounded in business needs.

  • Enthusiast about statistical methods, time-series forecasting, experimental design, and hypothesis testing.

  • Familiarity with database internals and building reliable ETL/ELT pipelines.

  • Experience optimizing queries and data systems for performance and cost.

What you could do

  • Design and maintain canonical datasets that serve as sources of truth for metrics across the entire company.

  • Build end-to-end data pipelines for analyzing petabytes of web search data.

  • Scale analytics across the company by building data products, self-serve tools, and internal BI tools for product, finance, marketing, and GTM teams.

  • Develop quantitative models that form the basis for our forecasting and experimentation.

  • Own the A/B testing infrastructure used to test hypotheses about our product and business.

Exa is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, creed, color, religion, sex, sexual orientation, gender identity or expression, national origin, disability, age, veteran status, marital status, pregnancy or related conditions, criminal histories consistent with applicable law, or any other basis protected by applicable law.

Skills Required

  • Strong proficiency in Python
  • Strong proficiency in SQL
  • Strong proficiency with cloud services for data processing, warehousing, and orchestration
  • Detail-oriented approach to establishing accurate metrics with analytical rigor
  • Ability to champion cross-functional data-driven decision-making, data integrity, and rigorous data requirements
  • Familiarity with statistical methods, time-series forecasting, experimental design, and hypothesis testing
  • Familiarity with database internals and reliable ETL/ELT pipelines
  • Experience optimizing queries and data systems for performance and cost
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The Company
HQ: Burlington, MA
86 Employees
Year Founded: 2021

What We Do

Exa was built with a simple goal — to organize all knowledge. After several years of heads-down research, we developed novel representation learning techniques and crawling infrastructure so that LLMs can intelligently find relevant information.

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