Senior Data Engineer
Employment Information
Job Description
Senior Data Engineer building and scaling the data platform for Native, driving data workflows across GCP services. Collaborating with analytics, product, and engineering teams to deliver insights.
Responsibilities:
- Design, build, and maintain scalable ETL pipelines and DAG-orchestrated workflows using GCP-native tools (e.g. Cloud Composer/Airflow, Dataflow, BigQuery) to support both operational and analytical needs
- Develop and evolve data models and schemas that enable clean, reliable, and well-structured downstream consumption
- Partner with analytics, product, and engineering teams to understand data requirements and deliver accessible, high-quality datasets
- Own the end-to-end lifecycle of data workflows, including deployment, monitoring, alerting, and troubleshooting in production environments
- Ensure data quality, consistency, and integrity through validation frameworks, monitoring, and adherence to engineering best practices (testing, version control, documentation)
- Optimize data processing for performance, scalability, and cost efficiency across GCP services
- Contribute to the evolution of the data platform architecture to ensure maintainability, scalability, and long-term reliability
- Stay current with emerging technologies and best practices in data engineering, workflow orchestration, and cloud data platforms
Requirements:
- 5+ years of experience in data engineering or similar roles
- Strong proficiency in SQL and Python
- Hands-on experience with GCP data infrastructure (BigQuery, Cloud Composer/Airflow, Dataflow, Cloud Storage, Pub/Sub)
- Experience designing and orchestrating DAG-based ETL/ELT pipelines
- Solid understanding of data modeling and data warehousing concepts
- Experience designing and optimizing data movement across GCP services (e.g., BigQuery ⇄ GCS, Pub/Sub pipelines, Dataflow transformations)
- Experience deploying, monitoring, and troubleshooting production data pipelines
- Familiarity with CI/CD, testing, and version control best practices in data ecosystems
- Ability to partner cross-functionally with analytics, product, and engineering teams
Benefits:
- Health insurance
- Flexible work arrangements
- Professional development
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