Data Engineer
$64,890–$173,040 year
RemoteUnited States
Job Summary
Build and maintain data pipelines and ETL/ELT processes using Python to enable self-service data ingestion for application teams. Write and optimize SQL queries for data warehouse transformation, troubleshooting, and validation while contributing to data modeling efforts within the Snowflake platform. Integrate with Kafka-driven event streams for real-time data ingestion and implement standardized logging, alerting, and escalation to improve pipeline reliability. Ensure thorough unit and integration test coverage, participate in code reviews, and validate pipeline correctness via CI/CD workflows using GitHub Actions. Respond to production alerts as part of the team on-call rotation and leverage AI development tools to improve development speed.
Required Qualifications
- 2 years of professional data engineering or software development experience
- Strong, demonstrated proficiency in SQL — including writing, optimizing, and troubleshooting complex queries against large datasets
- Experience working with at least one cloud data warehouse or data platform (Snowflake, BigQuery, Amazon Redshift, Azure Synapse Analytics, or Databricks)
- Proficiency with Git-based version control, including branching, pull requests, and code review workflows
- Familiarity with CI/CD concepts and participation in automated deployment workflows (GitHub Actions)
- Bachelor of Science in Computer Science, Data Engineering, Information Systems, or a related field, or equivalent work experience
Desired Qualifications
- Hands-on experience building and maintaining data pipelines or ETL/ELT processes using Python
- Familiarity with Snowflake — any exposure to querying, schema concepts, or warehouse basics is a plus; it is a core platform tool for this team
- Experience writing unit and integration tests for data pipeline components
- Any exposure to DBT (Data Build Tool) or similar transformation frameworks; DBT is a key part of our data workflow and familiarity is a strong plus
- Familiarity with Kafka or similar event-streaming platforms (RabbitMQ, AWS SNS/SQS)
- Familiarity with containerized development (Docker)
- Basic exposure to Terraform or infrastructure-as-code concepts
- Awareness of structured logging, pipeline health checks, and monitoring/alerting tooling
- Familiarity with REST API integration patterns
- Experience with AI-enhanced development tools such as Claude, Snowflake CoCo, or Copilot
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