Data Platform Engineer: BigQuery, dbt & AI Pipelines

wine collective; San Francisco (CA); Hybrid; USD 160,000 - 230,000; Full time

Benefits offered by this job

Hybrid work

Fresh lunch

Commuter support

Wellness stipend

Flexible PTO

Medical coverage

Parental leave

Equity package

Annual summit

Job summary

Collective in San Francisco is building a data platform powering analytics, reporting, and AI. We own end-to-end pipelines moving data from product, financial, and third-party sources into our BigQuery warehouse, with clean, well-documented tables for analysts and downstream tools.

You will work with product engineers, analysts, and business stakeholders to ensure data quality, reliability, and scalability. This hands-on role emphasizes ownership and long-term maintainability of the data stack.

Qualifications

• 5+ years of professional experience in data engineering, analytics engineering, or a closely related role.
• Expert-level SQL and strong Python for building pipelines, transformations, and tooling.
• Cloud data warehouse experience with BigQuery preferred; dbt or equivalent transformation framework.
• Deep understanding of dimensional modeling and layered warehouse architecture.
• Experience implementing testing, monitoring, and alerting for data pipelines in production.

Responsibilities

• Design and build data pipelines.
• Model the data.
• Own data quality and reliability.
• Optimize performance and cost.
• Establish engineering standards.
• Govern and secure data.
• Enable the business through reliable datasets and self-serve reporting.
• Support AI and analytics use cases.

Skills

SQL

Python

BigQuery

dbt

Data modeling

Data quality

CI/CD

Git workflows

Ownership

Communication

Tools

Fivetran

Airflow

Dagster

Terraform

Git

Job description

Collective in San Francisco is building a data platform powering analytics, reporting, and AI. We own end-to-end pipelines moving data from product, financial, and third-party sources into our BigQuery warehouse, with clean, well-documented tables for analysts and downstream tools.

You will work with product engineers, analysts, and business stakeholders to ensure data quality, reliability, and scalability. This hands-on role emphasizes ownership and long-term maintainability of the data stack.