Staff Analytics Engineer: Lead AI-Driven Data Platform

Socket.dev; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

4 weeks vacation

Parental leave

Medical coverage

Dental coverage

Vision coverage

Wellness stipend

Job summary

Render, a leading cloud platform for developers, seeks a Staff Analytics Engineer to guide the Analytics Engineering platform. You will shape data quality, semantic layers, and scalable models for analytics and AI-assisted decision-making.

Collaborating with analysts, scientists, and cross-functional teams, you’ll establish standards, drive reliability, and enable self-service analytics across the organization. Hybrid work with strong emphasis on scalability and performance.

Qualifications

• Experience in analytics engineering or data engineering with dbt in production.
• Strong SQL skills and ability to design scalable dimensional models.
• Knowledge of modern cloud data warehousing and governance practices.

Responsibilities

• Own the Analytics Engineering architecture and platform evolution.
• Build and maintain governed dbt models for trusted metrics.
• Define and improve data modeling, testing, and documentation practices.
• Enable self-service analytics with semantic and context layers.
• Collaborate with analysts, scientists, and business stakeholders.
• Improve data quality, warehouse architecture, and BigQuery performance.
• Mentor team members on dbt, dimensional modeling, and standards.

Skills

dbt

SQL

Data modeling

Git & CI/CD

Analytics engineering

Communication

Education

Bachelor's degree in a relevant field

Tools

BigQuery

Databricks

Snowflake

dbt

Job description

Render, a leading cloud platform for developers, seeks a Staff Analytics Engineer to guide the Analytics Engineering platform. You will shape data quality, semantic layers, and scalable models for analytics and AI-assisted decision-making.

Collaborating with analysts, scientists, and cross-functional teams, you’ll establish standards, drive reliability, and enable self-service analytics across the organization. Hybrid work with strong emphasis on scalability and performance.