Staff Data Engineer: Analytics Platform Lead (Remote)

Remote; San Francisco (CA); Remote; USD 160,000 - 230,000; Full time

Benefits offered by this job

Equity for U.S. employees

401(k) with company match

Home office stipend

Coworking Wallets

Annual professional development budget

Monthly Betterment stipend

Monthly telecommunications stipend

Flexible PTO

Job summary

Afresh is seeking a Staff Data Engineer to lead the analytics platform on the Data Science and Analytics team. You will evolve data models, pipelines, and governance while collaborating across the company to deliver reliable AI-assisted insights.

You will mentor engineers, own architecture decisions, and help scale data products that reduce waste and improve store-level decision making.

Qualifications

• 6+ years in data engineering or analytics engineering.
• Strong leadership and cross-functional collaboration.
• Ability to learn new domains and own problems.
• Fluency in SQL, data warehousing, and dimensional modeling.
• Experience with cloud data warehouses (Databricks, Snowflake, BigQuery).
• Experience with orchestration tools like Airflow/Dagster.
• Experience with semantic layers such as LookML, Cube.dev, or similar.
• Experience designing pipelines for billion+ row datasets.
• Experience integrating AI models to deliver reliable analytics.

Responsibilities

• Own development of the analytics platform and data models.
• Evolve data pipelines and governance patterns.
• Collaborate with stakeholders to build dimensional models and metrics.
• Mentor other engineers and foster knowledge sharing.

Skills

SQL

Dimensional modeling

Data warehousing

dbt

Airflow

Dagster

LookML

Cube.dev

Databricks

Communication

Leadership

ML integration

Tools

Airflow

Dagster

dbt

LookML

Cube.dev

Databricks

Job description

Afresh is seeking a Staff Data Engineer to lead the analytics platform on the Data Science and Analytics team. You will evolve data models, pipelines, and governance while collaborating across the company to deliver reliable AI-assisted insights.

You will mentor engineers, own architecture decisions, and help scale data products that reduce waste and improve store-level decision making.