Build and optimize analytical data models in BigQuery.
Implement partitioning, clustering, and materialized views for performance and cost efficiency.
Ensure compliance with data governance, access controls, and IAM best practices.
Develop integrations with external systems (APIs, flat files etc.) using GCP-native or hybrid approaches.
Utilize tools like Dataflow or custom Python/Java services on Cloud Functions or Cloud Run to handle transformations and ingestion logic.
Build automated CI/CD pipeline using Cloud Build, GitHub Actions, or Jenkins for deploying data pipeline code and workflows.
Set up observability using Cloud Monitoring, Cloud Logging, and Error Reporting to ensure pipeline reliability.

1+ years of experience with SQL, NoSQL
1+ years of experience with Python (or a comparable scripting language)
1+ years of experience with Data warehouses (such as data modeling and technical architectures) and infrastructure components
1+ years of experience with ETL/ELT, and building high-volume data pipelines
1+ years of experience with reporting/analytic tools
1+ years of experience with query optimization, data structures, transformation, metadata, dependency, and workload management
1+ years of experience with Big data and cloud architecture
1+ years of hands-on experience building modern data pipelines within a major cloud platform (GCP)
1+ years of experience with deployment/scaling of apps on containerized environment (i.e. Kubernetes, AKS)
1+ years of experience with real-time and streaming technology (i.e. Kafka, Spark Streaming)

Affordable medical plan options
401(k) plan (including matching company contributions)
Employee stock purchase plan
No-cost programs for all colleagues including wellness screenings, tobacco cessation and weight management programs
Confidential counseling and financial coaching
Paid time off
Flexible work schedules
Family leave
Dependent care resources
Colleague assistance programs
Tuition assistance
Retiree medical access

Data Engineer

Key skills