Gcp Data Engineer

Overview We are looking for an experienced Data Engineer with deep expertise in Google Cloud Platform (GCP) to design, build, and manage end-to-end data systems that drive analytics, business intelligence, and AI initiatives. In this role, youll be responsible for building high-performance data pipelines that handle data ingestion, transformation, and integration (ETL/ELT) across multiple systems. Beyond ETL, you will design the broader data infrastructure, including data lakes, workflow orchestration, API-driven integrations, and CI/CD automation to ensure scalability, reliability, and seamless data flow across the enterprise. This is a hands-on engineering position that blends data architecture, software engineering, and cloud infrastructure expertise. Youll collaborate closely with analytics, data science, and product teams to enable a modern, data-driven ecosystem. Key Responsibilities Design and develop scalable data pipelines using GCP services such as BigQuery, Dataflow (Apache Beam), Pub/Sub, Dataproc, and Cloud Composer (Airflow). Implement end-to-end ETL/ELT workflows that extract data from multiple sources, transform it into meaningful formats, and load it into data warehouses or data lakes. Architect and maintain data lake environments using Cloud Storage and integrate with downstream analytical tools. Automate data ingestion frameworks from APIs, streaming platforms, and external systems into the cloud. Implement workflow orchestration and job automation with Airflow/Cloud Composer to ensure smooth and reliable pipeline execution. Enable real-time data streaming and event-driven processing using Pub/Sub, Kafka, or Dataflow. Collaborate with analytics and ML teams to design data models, feature stores, and data marts that power reporting and AI solutions. Develop API-based data services and reusable connectors to enhance cross-platform data accessibility. Integrate CI/CD best practices for data pipelines, including testing, versioning, and automated deployment through Git, Terraform, and Cloud Build. Establish data governance, lineage, and observability frameworks to ensure data quality, security, and compliance. Required Skills & Experience 4–7 years of experience in data engineering or ETL development with large-scale data systems. Strong proficiency in Google Cloud Platform (BigQuery, Dataflow, Pub/Sub, Dataproc, Cloud Storage, Composer). Solid understanding of ETL/ELT design principles, data modeling, and data warehouse architecture. Proficiency in Python, SQL, and experience with distributed data processing frameworks (Apache Beam, Spark). Experience building data lakes and managing schema evolution in structured and semi-structured data (JSON, Parquet, Avro). Skilled in workflow orchestration (Airflow, Dagster, or Prefect) and infrastructure automation (Terraform, Cloud Deployment Manager). Familiarity with API integrations, event streaming, and real-time data processing. Google Professional Data Engineer Certification or equivalent. Experience with modern data stack tools like DBT, Looker, or Dataform. Familiarity with machine learning data pipelines or feature engineering workflows. #J-18808-Ljbffr