Staff Data Engineer – AI Analytics Platform Lead

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

Benefits offered by this job

Comprehensive Health & Wellness

401(k) with generous company match

Home office stipend

Coworking Wallets

Annual professional development budget

Betterment stipend

Flexible PTO

Job summary

Afresh is hiring a Staff Data Engineer to lead the analytics platform that powers our AI-driven grocery analytics. You will evolve data models, strengthen pipelines, and set data governance for internal and customer analytics. Collaborate with data scientists, product engineers, and operations to deliver reliable insights at scale.

The role emphasizes ownership, mentorship, and the ability to work across teams to drive data-driven decisions that reduce waste and improve store performance.

Qualifications

• 6+ years of experience as a data engineer, analytics engineer, or similar role.
• Proven ability to lead cross-team collaboration and data-driven decision making.
• Strong ownership and initiative in solving complex data problems.

Responsibilities

• Own the development of Afresh's analytics platform and evolve data models.
• Extend data pipelines and establish governance for internal and customer analytics.
• Collaborate with stakeholders to build extensible models and semantic metrics.
• Mentor and up-skill other engineers.

Skills

Data engineering

Leadership

Cross-team collaboration

Ownership

SQL & data modeling

Cloud data warehousing

dbt

Airflow

LookML / Looker models

AI integration

Tools

Databricks

Snowflake

BigQuery

dbt

Airflow / Dagster

Cube.dev

LookML

Job description

Afresh is hiring a Staff Data Engineer to lead the analytics platform that powers our AI-driven grocery analytics. You will evolve data models, strengthen pipelines, and set data governance for internal and customer analytics. Collaborate with data scientists, product engineers, and operations to deliver reliable insights at scale.

The role emphasizes ownership, mentorship, and the ability to work across teams to drive data-driven decisions that reduce waste and improve store performance.