Data Engineering Engineer

Skills:

Google Cloud Platform (BigQuery, Vertex AI, Cloud Run, CloudBuild, Secret Manager, Pub/Sub, DataFlow, DataForm, Dataproc, GIT Actions, Artifact Registry, Docker), Strong Python + SQL + Fast API, CI/CD Pipelines, Tekton/Terraform, Astronomer/Airflow Frontend (React, Streamlit etc,,)

Role & responsibilities

• As a Data Engineer, you will be responsible for designing, building, and maintaining the robust data architectures and scalable pipelines that power the organizations data-driven decision-making using Google Cloud Platform.
• You will transform raw, unstructured data into high-quality datasets by implementing efficient ETL/ELT processes, managing cloud-based data warehouses, and ensuring the reliability and security of the entire data infrastructure.
• Working at the intersection of software engineering and data science, you will collaborate with cross-functional teams to optimize data accessibility and performance, ultimately enabling Data Scientists and to derive actionable insights from complex information systems.
• Responsibilities: Design and implement scalable ETL/ELT pipelines using Python and SQL to ingest, transform, and load large-scale datasets into Google Cloud Storage and BigQuery. Orchestrate complex data workflows and DAGs through Astronomer/Airflow to ensure high availability and reliable scheduling of data processes.
• Utilize Terraform to manage and provision cloud infrastructure as code, ensuring consistent and reproducible environments across the Google Cloud Platform.
• Build and maintain automated CI/CD pipelines using Tekton/ GCP Cloud Native services to streamline the testing, containerization, and deployment of data services. Deliver AI-ready data products that are specifically structured and optimized with applying metadata on objects for machine learning consumption and advanced data analysis.
• Optimize BigQuery performance through strategic schema design, partitioning, and clustering to minimize costs and maximize query efficiency. Conduct deep-dive data analysis to validate data integrity and ensure that all outputs meet the rigorous quality standards required for production-level AI systems.
• Partner with cross-functional teams to translate business requirements into technical data specifications and highly curated data assets.