F2F in Phoenix, AZ | Minimum 07+ Yrs. experience after Graduation/Bachelors | Core GCP Cloud Data Engineer (Need minimum 03 Years of Core GCP Cloud Data Engineering experience.) (Phoenix)

Summary

• Design, build, and maintain scalable data pipelines and infrastructure on Google Cloud Platform (GCP), enabling reliable data flow for analytics, reporting, and downstream applications.

Key Responsibilities

• Design and develop ETL/ELT pipelines using GCP-native tools (Dataflow, Dataproc, Composer/Airflow, Cloud Functions).
• Build and optimize data models and warehouses in BigQuery.
• Manage data ingestion from batch and streaming sources (Pub/Sub, Cloud Storage, Kafka).
• Ensure data quality, validation, and governance across pipelines.
• Implement CI/CD for data workflows using Cloud Build, Terraform, or similar IaC tools.
• Monitor pipeline performance, troubleshoot failures, and optimize cost/query efficiency.
• Collaborate with data analysts, scientists, and product teams to understand data requirements.
• Maintain documentation of data architecture, lineage, and processes.
• Apply security and compliance best practices for data handling (IAM, encryption, access controls).

Required Skills & Qualifications

• Experience: 06-10 years in data engineering, with hands-on GCP experience.
• Core GCP Tools: BigQuery, Dataflow (Apache Beam), Cloud Composer (Airflow), Cloud Storage, Pub/Sub, Dataproc.
• Programming: Python and/or Java/Scala; strong SQL skills.
• Infrastructure: Experience with Terraform or Deployment Manager for IaC.
• Data Modeling: Dimensional modeling, star/snowflake schemas, partitioning/clustering strategies.
• Version Control/CI-CD: Git, Cloud Build, Jenkins, or similar.
• Certifications (preferred): Google Professional Data Engineer.

Nice to Have

• Experience with dbt for transformation workflows.
• Familiarity with Kafka or other streaming platforms.
• Exposure to ML pipeline integration (Vertex AI).
• Experience in multi-cloud or hybrid environments.

Soft Skills

• Strong problem-solving and analytical thinking.
• Clear communication with technical and non-technical stakeholders.
• Ownership mindset with attention to reliability and scalability.