We are looking for an experienced Data Engineer to help us improve data quality, fix existing data issues, and ingest more data from APIs and LLM-based sources to complement our current datasets.
You’ll proactively turn ambiguous requirements into well-structured engineering plans. You’ll communicate trade-offs and risks early, and you’ll verify outcomes through hands-on testing. You’ll bring a “build, measure, improve” mindset to performance, reliability, and user experience.
Design, build, and maintain dbt pipelines for our analytics and operational workloads
Build and maintain ETL/ELT processes to ingest data from multiple APIs and other external sources
Set up and manage workflows in orchestration platforms such as Dagster
Develop and refine our data models to support analytics, reporting, and downstream products
Diagnose and fix data quality issues (duplicates, missing fields, inconsistent formats, incorrect mappings, etc.)
Implement robust data cleaning and validation checks
Integrate LLM-based data enrichment (e.g., using OpenAI or similar APIs) to improve and complement event data
Collaborate with our product and ops team to understand data needs and translate them into technical solutions
Requirements:
Very high English proficiency (clear communication, strong writing, and the ability to collaborate effectively)
At least 3 years of data engineering experience including experience with dbt and the modern data stack
Some experience with devops, CI/CD, and database management.
At least 6 months of experience working exclusively in an agentic coding environment (e.g., Claude Code, Codex)
Ability to understand data engineering fundamentals, not just generate code—debugging, reasoning about behavior, and ensuring correctness
United States