Design, build, and maintain scalable data pipelines and ETL processes
Own data ingestion from product databases, third-party tools, and external data sources
Build and manage data models to support analytics, reporting, and operational workflows
Ensure data quality, reliability, and observability across the data stack
Partner with Product, Operations, Growth, and Leadership to define key metrics and data requirements
Support analytics and BI use cases by enabling clean, well-documented datasets
Optimize data storage, performance, and cost across the data platform
Implement best practices for data governance, access controls, and security
Troubleshoot and resolve data pipeline failures and inconsistencies
Contribute to technical documentation and improve data engineering standards over time

Requirements

2-5 years of experience in data engineering or backend engineering with a strong data focus
Strong experience with Python and/or similar languages
Hands-on experience building and maintaining ETL pipelines (e.g., Airflow, Dagster, dbt, Fivetran, or similar tools)
Experience with relational databases and modern data warehouses (e.g., Snowflake, BigQuery, Redshift)
Experience working in cloud environments (AWS, GCP, or Azure)
Strong understanding of analytics-friendly data modeling concepts
Comfortable owning data systems end-to-end, from design through production
Proactive and thoughtful engineer who can navigate ambiguity
Strong partner to cross-functional teams including Product, Ops, and Finance
Clear written and verbal communication skills
Thrives in a fast-paced, evolving startup environment

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data engineeringbackend engineeringETL processesdata modelingPythonAirflowDagsterdbtFivetranrelational databases

Soft Skills

proactivethoughtfulcommunicationcross-functional collaborationproblem-solvingadaptabilityownershipattention to detailnavigating ambiguityteamwork