In-Office Data Engineer — Pipelines & Recommendations

The Mill; San Bruno (CA); Hybrid; USD 185,000 - 210,000; Full time

Benefits offered by this job

Equity grant

Health insurance

401(k)

Job summary

The Mill is seeking a Data Engineer to design and operate scalable data pipelines across our product and operational systems. You will contribute to a customer-facing recommendation engine and manage data transformations from multiple sources, enabling actionable insights for teams.

You will work with Python, SQL, and modern tooling (dbt, Airflow, Fivetran, Snowflake/BigQuery/Redshift) and apply CI/CD, testing, and observability to maintain data quality.

Qualifications

• 5+ years of production data engineering experience.
• Built data pipelines with Python and tools like dbt, Airflow, or Fivetran.
• Strong SQL and cloud data warehouse experience (Snowflake/BigQuery/Redshift).
• Experience with recommendation systems or multi-source data pipelines using LLMs.
• Set up CI/CD for data pipelines and measured impact of changes.

Responsibilities

• Design, build, and maintain scalable data pipelines across product and ops.
• Operate the customer-facing recommender engine with LLM-based logic.
• Design integration pipelines for multi-source data and handle schema changes.
• Partner with analytics/marketing to support self-serve tools.
• Own data quality monitoring, alerts, and observability.
• Define metrics, endorsements, and business logic used company-wide.

Skills

Python

SQL

dbt

Airflow

Fivetran

CI/CD

LLMs

Tools

Snowflake

BigQuery

Redshift

Terraform

Pulumi

Hex

Tableau

Mixpanel

Job description

The Mill is seeking a Data Engineer to design and operate scalable data pipelines across our product and operational systems. You will contribute to a customer-facing recommendation engine and manage data transformations from multiple sources, enabling actionable insights for teams.

You will work with Python, SQL, and modern tooling (dbt, Airflow, Fivetran, Snowflake/BigQuery/Redshift) and apply CI/CD, testing, and observability to maintain data quality.