BI (Analytics) Engineer (Full-time - Hybrid)

Requirements

• 3 - 7 years of experience in analytics/data science at a high-growth company
• Built data models and pipelines at a VC-backed startup or big tech company founded after 2005 (e.g., not FAANG, but companies like Uber, Lyft, Stripe, Datadog)
• Experience partnering with product and operations teams on data needs
• Experience working in highly regulated industries (e.g., fintech, shipping/logistics, insurance) dealing with large, complex data sets

Education

• Bachelor's in CS, Math, Engineering, or hard sciences

Hard skills

• Advanced SQL in complex production data environments
• Hands on experience with common data warehouses (e.g., Snowflake, BigQuery, or Redshift)
• Hands-on dbt experience for data modeling in production
• Familiarity with Google Cloud Platform/BigQuery, Python, or Terraform

Miscellaneous

• Able to work 4 days/week in-office in Denver, SF, or NYC

Traits to avoid

• Only worked at large, slow-moving enterprises (e.g., UnitedHealth, legacy health systems)
• Short tenures (<1 year) at multiple companies
• Seeking management or leadership scope over IC work

Role

• We are looking for a BI/Analytics Engineer with 4+ years of experience to own data modeling and pipelines at a rapidly scaling data company disguised as a healthcare platform. You'll join a lean but high-impact data team (currently just 3 people supporting 70+ engineers) and be responsible for building the foundational data layer that powers customer-facing reporting and future ML/AI products. This is a hands-on-deck role where you'll shape messy, complex data models into something reliable and scalable – working closely with product, operations, and engineering teams. The ideal candidate thrives in ambiguity, can quantify their impact, and brings the pace and ownership mindset of a high-growth startup.

What you will be doing

• Owning and maturing the data modeling practice – building critical models and pipelines that power customer-facing reporting and analytics
• Scaling and cleaning up existing data infrastructure to support increasingly large and complex enterprise customer integrations
• Partnering closely with product management and internal operations teams to understand data needs and deliver accurate, trusted outputs
• Deep diving with engineers to understand complex production data and ensuring visualization platforms (Metabase) are accurate and available
• Setting the foundation for future ML and AI products by improving data quality, governance, and pipeline reliability

Visa Sponsorship Details

• Open to visa transfers (e.g. OPT, H1B transfers)
• Open to visa sponsorships (e.g. new H1B, TN)

Ask candidates

• Walk me through a project where you owned data modeling end-to-end at a high-growth startup. What was the business impact and how do you quantify it?
• How have you partnered with product teams or non-technical stakeholders to translate complex data work into something they can act on?

Interview

• Head of Technical Recruiting (30 minutes)
• SQL Technical Screen (1 hour)
• Data Modeling Exercise (1 hour)
• Product Team Member (1 hour)
• Head of Engineering Interview (45 minutes)
• Executive Who Interview w/ Co-Founder or Senior Leader (1 hour)

“Cleo Consulting is an equal opportunity employer (Minorities/Women/Veterans/Disabled)”