The Select Group · 6 hours ago
Sr Data Engineer
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Responsibilities
Design, implement, and optimize cloud-based data pipelines using GCP tools and services (e.g., BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Storage) to ensure seamless data processing, storage, and analysis.
Develop and maintain robust CI/CD pipelines for data processing and data integration workflows using tools like Cloud Build, Jenkins, Terraform, or similar. Ensure automated testing, deployment, and monitoring for end-to-end data pipeline performance.
Design data architectures that are highly available, fault-tolerant, and scalable to support large datasets, real-time data processing, and data warehousing solutions on GCP.
Work closely with cross-functional teams to understand business requirements and deliver data solutions that align with strategic objectives. Mentor and provide technical guidance to junior data engineers.
Build automated processes for data ingestion, transformation, and quality assurance. Optimize data workflows for performance, cost, and efficiency.
Implement monitoring and alerting mechanisms for data pipelines, identifying performance bottlenecks and operational issues, and taking proactive steps to address them.
Implement best practices for data governance, data quality, and security across the GCP data stack. Ensure compliance with organizational standards and industry regulations.
Qualification
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Required
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
5+ years of experience in data engineering, with a focus on cloud technologies (GCP preferred).
Proficiency with GCP services including BigQuery, Cloud Storage, Dataflow, Pub/Sub, and Dataproc.
Strong experience in designing, building, and optimizing data pipelines and workflows.
Hands-on experience with CI/CD tools (e.g., Jenkins, Cloud Build, GitLab CI) and automation frameworks (e.g., Terraform, Ansible).
Solid understanding of containerization and orchestration (Docker, Kubernetes).
Proficient in programming languages such as Python, Java, or SQL for data processing and pipeline automation.
Experience with data warehousing concepts and technologies (e.g., Google BigQuery).
Familiarity with monitoring and logging tools (e.g., Stackdriver, Prometheus, Grafana).
Experience in agile development methodologies and working in collaborative, cross-functional teams.
Preferred
Certification in Google Cloud (e.g., Google Cloud Professional Data Engineer) is a plus.
Experience with data orchestration tools such as Apache Airflow, Prefect, or Dagster.
Knowledge of data lakes, ETL processes, and real-time data streaming technologies.
Familiarity with data privacy and security best practices.