GCP Data Engineer

Job Description: Years of Experience : 6-10 Years

Key Responsibilities

Design, build, and optimize scalable ETL/ELT data pipelines across GCP. Develop modular, object‑oriented Python applications for data ingestion, orchestration, and transformation. Architect, deploy, and maintain data solutions leveraging GCP services: BigQuery, GCS, Cloud Functions, Cloud Run, Cloud Composer, Pub/Sub, Eventarc, and DataForm. Integrate with external APIs to ingest datasets into BigQuery and other storage layers. Implement event-driven and real-time data processing architectures. Build and automate CI/CD workflows for deploying pipelines and services using Cloud Build. Ensure optimal performance, scalability, and cost efficiency of pipelines and data infrastructure. Establish and enforce data quality, governance, lineage, and security standards. Work closely with cross-functional teams to convert business needs into scalable data solutions. Maintain detailed technical documentation and contribute to engineering best practices.

Must have skills Proven experience building modular, scalable ETL/ELT pipelines. Hands-on expertise with GCP services such as BigQuery, Cloud Functions, Cloud Run, Cloud Composer, GCS, and Pub/Sub. Hands-on Experience in Airflow Ability to design and implement real-time event-driven architectures using Pub/Sub and Eventarc. Experience integrating and consuming REST APIs for data ingestion. Strong understanding of data governance, quality, privacy, and security practices. Experience automating builds and deployments using CI/CD pipelines (Cloud Build preferred). Ability to collaborate effectively with data science, product, engineering, and compliance teams. Strong proficiency in Python with solid OOP principles, including encapsulation, inheritance, polymorphism, abstraction, SOLID, and design patterns. Strong problem-solving, debugging, and system design skills.