Data Platform Engineer; GCP Focus

Role: Data Platform Engineer (GCP Focus)

• Design, build, and automate scalable, production-grade data pipelines on GCP using core services such as Cloud Composer (Airflow), Dataflow (Apache Beam), and Big Query.
• Develop and implement continuous integration and continuous deployment (CI/CD) workflows for data processing and analytics pipelines using tools like Cloud Build, Git Hub Actions, or Jenkins.
• Implement reusable data frameworks, templates, and libraries to standardize pipeline deployments, configuration management, and promote "Infrastructure as Code" principles.
• Orchestrate complex, multi-stage ETL/ELT pipelines across diverse data sources and environments, ensuring efficient resource utilization and low latency.
• Implement and manage automated data validation, schema checks, and anomaly detection using tools like Great Expectations, dbt tests, or custom Python frameworks.
• Integrate quality gates directly into CI/CD workflows to ensure early issue detection and continuously improve overall data reliability.
• Schedule, monitor, and optimize data workflows, ensuring strict adherence to data delivery SLAs.
• Set up and maintain proactive monitoring, logging, and automated alerting for all data pipelines and platform components.
• Develop and maintain comprehensive dashboards to track critical metrics, including data health, SLA adherence, and pipeline operational performance.
• Integrate and manage data assets, schemas, and metadata within Google Data Catalog or equivalent metadata management platforms.
• Enforce robust governance policies, including data lineage tracking, strict access control (IAM), and compliance standards for sensitive data.

• 5+ years of professional experience in data engineering, data platform operations, or a similar cloud-native technical role.
• Strong expertise in the Google Cloud Platform (GCP) data stack:
  Big Query, Dataflow, Cloud Composer, Pub/Sub, Cloud Functions, and Cloud Build.
• High proficiency in Python, SQL, and general automation scripting.
• Hands‑on experience with CI/CD principles and tools, including Git Ops and Infrastructure as Code (IaC) using Terraform or Cloud Deployment Manager.
• Proven experience with data quality and testing frameworks such as Great Expectations, dbt, or PyTest.
• Working knowledge of observability, logging, and monitoring frameworks for high‑volume data systems.
• Familiarity with metadata management, data lineage tools, and establishing data governance policies.

Graduate; 5‑8 years of experience;
Mid‑Senior level;
Full‑time;
Engineering and Information Technology; IT Services and IT Consulting.