Analytics Engineer: Data Modeling & Pipelines (Hybrid)

raydar; San Francisco (CA); Hybrid; USD 135,000 - 180,000; Full time

Benefits offered by this job

Flexible hybrid schedule

Relocation support

Job summary

Raydar, a fast-growing healthcare technology company, seeks an Analytics Engineer to own data modeling and pipelines that drive reporting and ML-ready datasets. You will collaborate across product, operations and engineering to transform complex production data into reliable assets and scalable dashboards.

You will lead data modeling, clean and scale data infra, partner with cross-functional teams, and lay groundwork for future ML initiatives while maintaining a stable analytics stack.

Qualifications

• 4+ years owning data modeling and pipelines.
• Advanced SQL in production environments.
• Experience building data models and pipelines at a tech company founded after 2005.
• Hands-on with Snowflake, BigQuery or Redshift.
• Bachelor’s degree in CS, math, engineering or hard science.
• Ability to describe business impact with quantified outcomes.
• Comfort with ambiguity in a fast-paced, ownership-driven environment.
• Office in SF four days per week.

Responsibilities

• Lead and mature the data modeling practice, building core models and pipelines for reporting and analytics.
• Clean up and scale existing data infrastructure for larger, more complex integrations.
• Collaborate with product and ops to provide precise, dependable datasets.
• Work through production data with engineers and keep dashboards correct and available.
• Prepare the ground for future ML/AI by strengthening data standards and pipeline stability.

Skills

Advanced SQL

Data modeling

Data pipelines

Data warehouse

Business impact

Education

Bachelor's degree (CS/math/engineering/hard science)

Tools

dbt

Python

Terraform

Job description

Raydar, a fast-growing healthcare technology company, seeks an Analytics Engineer to own data modeling and pipelines that drive reporting and ML-ready datasets. You will collaborate across product, operations and engineering to transform complex production data into reliable assets and scalable dashboards.

You will lead data modeling, clean and scale data infra, partner with cross-functional teams, and lay groundwork for future ML initiatives while maintaining a stable analytics stack.