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The Data Engineer will design, develop, and maintain data solutions leveraging Google Cloud Platform (GCP) and Python. This role is critical for building scalable data pipelines, optimising performance, and ensuring data integrity across multiple systems. The individual will collaborate with cross-functional teams to deliver high-quality data products that support business decisions.
Job Responsibility:
Design, develop, and maintain ETL/ELT pipelines to ingest, transform, and load large datasets into GCP-based platforms (e.g., BigQuery, Cloud Storage)
Optimise data pipelines for performance, reliability, and scalability
Develop and manage data models, schemas, and storage solutions aligned with best practices
Leverage GCP services such as Cloud Composer, Dataflow, Pub/Sub, and Cloud Functions to build automated workflows
Implement data validation, cleansing, and quality checks to maintain accuracy and integrity
Collaborate with data scientists, analysts, and business stakeholders to define and execute data requirements
Set up monitoring systems to track pipeline performance and ensure timely delivery
Requirements:
Proficiency in Python for building and deploying data processing scripts
Strong expertise in GCP services, especially BigQuery, Cloud Storage, Cloud Functions, Cloud Composer, and Pub/Sub
Experience with SQL for querying and processing data
Familiarity with workflow orchestration tools like Apache Airflow
Knowledge of version control systems (e.g., Git)
Excellent communication and stakeholder management skills
Nice to have:
Advanced GCP features and emerging cloud technologies
Best practices for cost optimisation and performance tuning in cloud environments
Integration of streaming solutions like Kafka with Python SDK
Collaborative approaches to data-driven decision-making