BI (Analytics) Engineer

About the role

This role connects analytics engineering, business intelligence, and data analysis to support data products. The position establishes foundational work for scaling the data team within a high-growth healthcare context.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Data models are built and improved to support reporting, analytics, and varied use cases for downstream data products. These standards advance operational excellence in data extraction and outcomes.

Data infrastructure is enhanced to support long-term product strategy and prepare for future machine learning and artificial intelligence capabilities. This work enables subsequent data initiatives.

Tooling and visualization platforms are validated for accuracy and availability across workflows.

Requirements

A bachelor's degree in a data-intensive field such as Math, Science, Engineering, or Library Science is required.

At least 4 years of experience in Data Science, Data Analytics, or Data Engineering is required within a high-growth startup or a scaled technical organization.

Hands-on experience with data models, data pipelines, and performing analysis is necessary.

Hands-on experience with common data warehouses such as Snowflake, BigQuery, or Redshift is required.

Strong SQL capability in complex data environments is required.

Experience or exposure with Google Cloud Platform (BigQuery), Metabase, Terraform, Python, and DBT is required.

Practical notes

The role is based in San Francisco with full-time engagement. Compensation is listed as $135,000 to $180,000 USD, with total compensation potentially including equity and benefits. Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Analytics engineering roles blend data modeling, pipeline development, and insight delivery. Professionals in this field commonly use SQL, cloud data platforms, and transformation tools. Clear communication of technical concepts to diverse stakeholders is a common expectation.

Questions to ask

Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.

About the company

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