Data Engineer
Location
United States
Posted
38 days ago
Salary
Not specified
Job Description
Role Description
Our data platform is scaling rapidly, and we need an engineer who can own pipelines end-to-end, keep data quality high, and ensure reliability as we grow. This role exists to strengthen our data infrastructure, accelerate delivery through automation, and ensure our B2B customers receive accurate, timely data they can trust.
You'll work on data systems that directly power customer workflows - where pipeline reliability and data quality directly impact retention.
- Build and maintain production-ready data pipelines using DBT, Snowflake, and modern orchestration tools.
- Own data engineering features end-to-end, from implementation through optimization and deployment.
- Fix and improve existing pipelines - identify bottlenecks, resolve issues, and enhance performance.
- Drive automation initiatives across the data stack to accelerate delivery and reduce manual interventions.
- Provide 2nd line support for B2B customers - investigate data issues, clarify edge cases, and ensure customers can trust their data.
- Design and implement new data import pipelines as we expand our data source coverage.
- Implement data quality improvements - validation, monitoring, and testing to ensure reliable, accurate data delivery.
- Contribute to code reviews, architectural discussions, and data engineering best practices.
Qualifications
- 3+ years of professional data engineering experience.
- Strong fundamentals in SQL, data modeling, Python and ETL/ELT principles.
- DBT - hands-on experience building and maintaining transformation pipelines.
Requirements
- Nice to have: Snowflake
- Nice to have: Databricks
- Nice to have: AWS (S3, Lambda, Glue, etc.)
- Nice to have: Prefect or similar orchestration tools (Airflow, Dagster)
- Solid understanding of data quality principles, testing strategies, and monitoring practices.
- Comfortable working in a fast-moving, remote-first environment.
- Strong communicator - able to explain technical issues clearly to both technical and non-technical stakeholders.
- Async-first mindset - can work independently, document decisions, and keep stakeholders informed without constant synchronous communication.
- End-to-end ownership mentality - you see tasks through from planning to production, handling blockers and follow-through.
- You care about data quality, pipeline reliability, and long-term maintainability.
Benefits
- Competitive compensation based on experience.
- Meaningful ownership and long-term growth opportunities.
- Flexible working hours.
- Fully remote-friendly team.
- Direct collaboration with founders and core engineering leadership.
Job Requirements
- 3+ years of professional data engineering experience.
- Strong fundamentals in SQL, data modeling, Python and ETL/ELT principles.
- DBT - hands-on experience building and maintaining transformation pipelines.
- Nice to have: Snowflake
- Nice to have: Databricks
- Nice to have: AWS (S3, Lambda, Glue, etc.)
- Nice to have: Prefect or similar orchestration tools (Airflow, Dagster)
- Solid understanding of data quality principles, testing strategies, and monitoring practices.
- Comfortable working in a fast-moving, remote-first environment.
- Strong communicator - able to explain technical issues clearly to both technical and non-technical stakeholders.
- Async-first mindset - can work independently, document decisions, and keep stakeholders informed without constant synchronous communication.
- End-to-end ownership mentality - you see tasks through from planning to production, handling blockers and follow-through.
- You care about data quality, pipeline reliability, and long-term maintainability.
Benefits
- Competitive compensation based on experience.
- Meaningful ownership and long-term growth opportunities.
- Flexible working hours.
- Fully remote-friendly team.
- Direct collaboration with founders and core engineering leadership.
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