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Mid-level data platform role with broad GCP/Kafka/dbt skillset increases competition.
GCP, Kafka, and dbt specialization limits portability but core data platform skills remain transferable.
Mandatory GCP, Kafka, dbt, Terraform, and specific language skills make shortlisting highly selective.
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Build and maintain batch and real-time data pipelines using Dataflow and Kafka for an Operational Data Store.
Develop and monitor Airflow (Cloud Composer) DAGs to ensure timely data delivery and deploy GCP resources using Terraform for environment consistency.
Create internal APIs and microservices using FastAPI and Golang, and implement data modeling with dbt on BigQuery.
Proficient in Python (Advanced), Golang (Intermediate), and SQL.
Experience with GCP services including Cloud Run, BigQuery, Cloud SQL for PostgreSQL, Cloud Spanner, Cloud Storage, Airflow (Cloud Composer), Pub/Sub, Eventarc, Artifact Registry, and Secret Manager.
Bachelor’s or Master’s degree in Computer Science, Data Engineering, Software Engineering, or a related technical field with strong coursework in Distributed Systems, DBMS, Algorithm Design, and Real-time Computing.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in designing and operating decoupled event-driven data architectures and complex data pipelines.
Comfortable managing infrastructure as code on GCP and deploying containerized applications with CI/CD pipelines.
Capable of developing backend services and data transformation pipelines using modern technologies (FastAPI, Golang, dbt) in a cloud-native environment.