Senior Data Engineer with GCP

Senior Data Engineer role focused on designing, implementing, and maintaining scalable data platforms on Google Cloud Platform to support analytics and data-driven products. The position involves partnering with cross-functional stakeholders to translate business requirements into robust, secure, and cost-effective data solutions, contributing to technical strategy and team capability.

• Bachelor’s degree in Computer Science, Engineering, Mathematics or equivalent practical experience, or relevant professional qualifications.
• Several years of hands-on experience in data engineering or an equivalent role, with demonstrable experience on Google Cloud Platform.
• Proficiency in SQL and at least one programming language commonly used for data engineering (e.g., Python, Java, Scala).
• Strong practical experience with BigQuery, GCP.
• Familiarity with workflow orchestration tools (e.g., Airflow / Composer) and modern ELT/ETL patterns.
• Experience with infrastructure-as-code (Terraform or similar), containerization and cloud deployment practices.
• Understanding of data modelling, data warehousing concepts and streaming architectures.
• Good communication skills and fluent English.

• Develop and maintain data pipelines (Bronze → Silver → Gold) using SQL-based ELT patterns.
• Implement data ingestion from multiple sources (SFTP, APIs, databases) into BigQuery (raw, append-only).
• Build and optimize transformation logic for curated Mart (Silver) layer using dimensional modeling principles.
• Create fact and dimension tables, including SCD handling and surrogate key logic.
• Ensure data quality checks are implemented and integrated with pipeline execution (row counts, reconciliation, validations).
• Support Airflow DAG development for orchestration, scheduling, and monitoring of pipelines.
• Enable reprocessing and idempotency, ensuring pipelines can be reliably rerun from Bronze.
• Collaborate on schema evolution and deployments using CI/CD (Git, Flyway, Jenkins).
• Optimize BigQuery performance and cost (partitioning, clustering, query tuning).
• Implement and maintain data integrations with downstream systems (e.g., ERP, reporting tools).
• Contribute to metadata, lineage, and documentation in the data catalog.
• Support testing, reconciliation, and parallel run validation during migration from legacy systems.
• Troubleshoot pipeline failures and support monitoring and alerting processes.