Data Engineer
Summary
Build and maintain scalable ETL/ELT pipelines and cloud data platforms, ensuring data quality and security while collaborating with analytics and ML teams.
Key Responsibilities
- Design, build, and maintain scalable ETL/ELT data pipelines.
- Develop and optimise data integration processes from multiple internal and external data sources.
- Build and maintain cloud-based data platforms and data warehouses.
- Ensure data quality, integrity, governance, and security across the data estate.
- Work closely with Data Scientists and Analytics teams to provide high-quality datasets for reporting and machine learning initiatives.
- Monitor, troubleshoot, and optimise data pipeline performance.
- Contribute to the design and implementation of modern data architecture and best practices.
- Support ongoing data transformation and digital innovation projects.
- Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions.
About You
- Experience working as a Data Engineer or in a similar data‑focused role.
- Strong SQL skills and experience working with large datasets.
- Experience building ETL/ELT pipelines.
- Knowledge of Python for data engineering and automation.
- Experience with cloud platforms such as Azure, AWS, or Google Cloud.
- Experience working with modern data warehouses such as Snowflake, Azure Synapse, Redshift, or BigQuery.
- Familiarity with orchestration tools such as Airflow, Azure Data Factory, or similar.
- Experience with version control tools such as Git.
- Understanding of data modelling and database design principles.
- Excellent problem‑solving and communication skills.
Desirable Experience
- Experience with Databricks or Apache Spark.
- Exposure to streaming technologies such as Kafka.
- Experience supporting AI, Machine Learning, or Advanced Analytics projects.
- Knowledge of CI/CD practices and Infrastructure as Code.
- Experience working within Agile environments.
What's on Offer
- Competitive salary and benefits package.
- Hybrid and flexible working arrangements.
- Opportunity to work on enterprise‑scale data and cloud transformation projects.
- Exposure to modern cloud technologies and AI initiatives.
- Clear career progression and professional development opportunities.
- Collaborative and innovative working environment.
GCS is acting as an Employment Business in relation to this vacancy.