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Role: Data Engineer with Google Cloud Platform
Location: Charlotte, NC (ONSITE)
Key Responsibilities
Architect and own scalable, secure, cloud-native data platforms on Google Cloud Platform Design, build, and optimize batch and real-time data pipelines using BigQuery, Dataflow, Pub/Sub, and Dataproc Lead BigQuery performance tuning and cost optimization (partitioning, clustering, query efficiency)
Orchestrate workflows using Cloud Composer (Apache Airflow)
Enable Al/ML and GenAl integration via Vertex Al and BigQuery ML
Enforce data governance, security, reliability, and FinOps best practices
Mentor engineers, conduct design/code reviews, and set enterprise data engineering standards
Role: Data Engineer with Google Cloud Platform
Location: Charlotte, NC (ONSITE)
Key Responsibilities
Architect and own scalable, secure, cloud-native data platforms on Google Cloud Platform Design, build, and optimize batch and real-time data pipelines using BigQuery, Dataflow, Pub/Sub, and Dataproc Lead BigQuery performance tuning and cost optimization (partitioning, clustering, query efficiency)
Orchestrate workflows using Cloud Composer (Apache Airflow)
Enable Al/ML and GenAl integration via Vertex Al and BigQuery ML
Enforce data governance, security, reliability, and FinOps best practices
Mentor engineers, conduct design/code reviews, and set enterprise data engineering standards
- Collaborate with product, analytics, and data science teams to deliver business-critical insights
- Google Cloud Platform Data Services: BigQuery, Dataflow (Apache Beam), Pub/Sub, Cloud Storage, Cloud Composer, Dataproc
- Programming & SQL: Advanced SQL, Python (Java/Scala a plus)
- Data Engineering: ETL/ELT, streaming & batch processing, data modeling, distributed systems
- Modern Architectures: Lakehouse, Apache Iceberg, Data Mesh concepts
- Al/ML Enablement: Vertex Al, BigQuery ML, GenAl-ready pipelines DevOps & laC: Terraform, CI/CD, DataOps practices
- Certification: Google Cloud Professional Data Engineer (strongly preferred / often mandatory)