Data Engineer

Maintain and improve our data pipelines to keep data flowing reliably from ingestion to delivery

Optimize performance and costs across Snowflake, Clickhouse, AWS, dbt, and Dagster

Own data quality by implementing monitoring, alerting, and validation

Manage and extend our infrastructure, including orchestration and CI/CD for data workflows

Support MLOps as we develop and productionize machine learning models

Be on call for critical pipelines and respond quickly to issues

Collaborate with data scientists, product managers, and engineers to launch new data products

Proven experience designing, building, and maintaining production data pipelines

Proficiency in Python and SQL

Hands-on experience with dbt, orchestration frameworks (such as Dagster, Airflow, or Prefect), and AWS

Familiarity with Snowflake, Clickhouse, or other modern data warehouses

Strong problem-solving skills and a proactive mindset

Experience with or interest in MLOps and supporting ML workflows in production

A sense of ownership and accountability for data quality and reliability

Access to some of the most interesting and rare data assets in AI, based on real human interactions with large language models

The chance to own and shape Profound’s data platform at an early stage

The opportunity to work on meaningful problems at the intersection of AI and marketing