BI (Analytics) Engineer

engineeringjobs.net, Inc.; New York (NY); On-site; USD 120,000 - 180,000; Full time

Job summary

Our client, a fast-growing data-driven healthcare insurance startup, is building a lean data team that supports 70+ engineers. The role focuses on owning data modeling and pipelines to power customer-facing reporting and ML/AI products.

You'll transform messy data into scalable foundations, collaborate with product, operations, and engineering, and drive measurable impact in a high-velocity startup environment.

Qualifications

• 3-7 years of experience in analytics or data science at a high-growth company.
• Bachelor's degree in CS, Math, Engineering, or a hard science.
• Hands-on experience with a major data warehouse (Snowflake, BigQuery, or Redshift).
• Ability to clearly articulate your business impact with quantified outcomes.

Responsibilities

• Own and mature the data modeling practice, building the critical models and pipelines behind customer-facing reporting and analytics.
• Scale and clean up existing data infrastructure to support larger, more complex enterprise customer integrations.
• Partner with product management and operations to understand data needs and deliver accurate, trusted outputs.
• Work directly with engineers to understand complex production data, and keep Metabase dashboards accurate and available.
• Lay the groundwork for future ML and AI products by improving data quality, governance, and pipeline reliability.

Skills

Advanced SQL

Data modeling

ETL pipelines

dbt

Python

BigQuery

Terraform

Education

Bachelor's degree in CS/Engineering

Tools

Snowflake

Redshift

Job description

Our client is a fast-growing healthcare insurance startup. Look past the healthcare label and you'll find a data company at its core. The data team is lean and high-impact: three people supporting 70+ engineers.

The Role

You'll own data modeling and pipelines, building the foundational data layer that powers customer-facing reporting today and ML/AI products tomorrow. This is a hands-on role. You'll take messy, complex data models and turn them into something reliable and scalable, working closely with product, operations, and engineering. If you thrive in ambiguity, work at startup pace, and can point to measurable impact, you'll fit right in.

Responsibilities:

• Own and mature the data modeling practice, building the critical models and pipelines behind customer-facing reporting and analytics
• Scale and clean up existing data infrastructure to support larger, more complex enterprise customer integrations
• Partner with product management and operations to understand data needs and deliver accurate, trusted outputs
• Work directly with engineers to understand complex production data, and keep Metabase dashboards accurate and available
• Lay the groundwork for future ML and AI products by improving data quality, governance, and pipeline reliability

Must-Have: (Dealbreakers):

• Experience building data models and pipelines at a VC-backed startup or a big tech company founded after 2005 (think Uber, Lyft, Stripe, or Datadog; FAANG doesn't count)
• Advanced SQL in complex production data environments
• Willingness to work in-office 4 days/week in Denver, SF, or NYC

Baseline Qualifications:

• 3-7 years of experience in analytics or data science at a high-growth company
• Bachelor's degree in CS, Math, Engineering, or a hard science
• Hands-on experience with a major data warehouse (Snowflake, BigQuery, or Redshift)
• Ability to clearly articulate your business impact with quantified outcomes

Nice-to-Have:

• Experience partnering with product and operations teams on data needs
• Experience in a highly regulated industry (fintech, shipping/logistics, insurance) working with large, complex data sets
• Hands-on dbt experience for production data modeling
• Familiarity with GCP/BigQuery, Python, or Terraform