Gcp Data Engineer
3 days ago
Required Qualifications
- Expert-level Python for backend/data engineering
- Hands-on GCP: Dataflow, BigQuery, Cloud Composer/Airflow, Cloud Functions, Cloud Run, GCS, IAM
- Design and development of ETL/ELT pipelines (batch and streaming)
- Apache Spark for large-scale processing
- Apache Kafka for messaging/streaming
- Orchestration with Airflow/Cloud Composer (DAG design, scheduling, monitoring)
- Strong SQL with enterprise RDBMS (SQL Server, Oracle, PostgreSQL)
- Git/GitHub and CI/CD for data projects
- Deployments on GKE and Cloud Run; understanding of autoscaling and load balancers
- API development with Python FastAPI; APIGEE proxy management
- Data quality frameworks, validation rules, monitoring/observability
- Security: IAM roles/policies, data redaction, DLP
- Data ingestion from varied sources; transformation and cleansing
- Documentation of pipelines, data flows, and operations
- Experience migrating on-prem data to cloud
Responsibilities
- Design, develop, test, and maintain scalable ETL/ELT data pipelines in Python
- Architect data solutions using Kafka, GCP services (Dataflow, BigQuery, Composer/Airflow, Functions, Cloud Run/GKE, GCS, IAM), dbt, and related tools
- Implement streaming and batch processing; ensure autoscaling, reliability, and cost efficiency
- Develop and manage Airflow/Cloud Composer DAGs for orchestration (scheduling, monitoring, alerting)
- Build APIs with FastAPI and manage APIs via APIGEE; integrate load balancers when required
- Ingest data from diverse sources; apply transformation, cleansing, and enrichment
- Implement data quality checks, validation rules, observability, and monitoring
- Apply security best practices including IAM, data redaction, and DLP
- Write and optimize complex SQL for extraction, validation, and analytics
- Manage code in GitHub; implement CI/CD pipelines for data workloads
- Deploy and operate workloads on GKE and Cloud Run
- Collaborate with data science/analytics teams to deliver fit-for-purpose data products
- Create and maintain documentation of pipeline designs, data flows, and operational runbooks
- Lead or contribute to on-prem to cloud data migration initiatives
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