Software Engineer Practitioner

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About The Role

Full Stack Data Engineer

Google Cloud Platform (GCP)

BigQuery, Dataform, Dataflow, and Pub/Sub

Key Responsibilities

• Design, develop, and maintain robust, scalable data pipelines on GCP using BigQuery, Dataform, Dataflow, and Pub/Sub.
• Collaborate with data engineering, architecture, and product teams to build data models, solutions, and automation.
• Ensure data governance, security, auditability, and high performance across all pipelines.
• Build custom cloud-native solutions leveraging tools like Data Fusion, Airflow, and Terraform.
• Optimize data transformation workflows using Python (NumPy, Pandas, PySpark, etc.) and SQL.
• Engage with stakeholders to understand data requirements and translate them into scalable engineering solutions.
• Participate in Agile/Scrum processes, including writing user stories and contributing to sprint planning.
• Drive the adoption of best practices in data warehousing, data lake design, and DevOps.
  

Must-Have Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 5+ years of experience in full stack data engineering and database performance optimization.
• Strong command of GCP native tools: BigQuery, Dataform, Dataflow, Pub/Sub, Data Fusion.
• Proficient in Python, Java, SQL, and data orchestration tools like Airflow.
• Experience with Terraform, Tekton, and version control (e.g., Git).
• Solid understanding of data architecture, ETL/ELT pipelines, and data governance.
• Deep familiarity with Agile methodology, DevOps, and collaborative product development.
• Excellent communication skills and stakeholder engagement experience.
  

Preferred Skills (Nice To Have)

• Experience with PostgreSQL, Dataproc, Cloud SQL, and containerization tools.
• Knowledge of industrial or enterprise data products and real-time data streaming.
• Experience working in a regulated or large enterprise environment.
  

Additional Information

• Interact with internal data and analytics product lines to identify technical opportunities.
• Influence design standards and ensure reusability and scalability of developed components.
• Support process improvements across data delivery, curation, and analytics operations.