Our Client and the Role:

Our client is a leading consultancy that supports organisations nationwide on Data Insights and Analytics projects, leveraging cutting-edge technologies. This company takes pride in delivering value and offering innovative alternatives.
 
Our client is seeking a skilled Data Platform Engineer to support a key client engagement. This is a hands-on, BAU role embedded within a high-performing data team, focused on developing and maintaining robust, scalable pipelines using Snowflake, dbt, and cloud-native tools.
 
Key Responsibilities:

• Designing and optimising end-to-end data pipelines using Snowflake, dbt, and supporting technologies.
• Applying Kimball methodology to build and maintain dimensional models and scalable warehousing solutions.
• Developing CI/CD pipelines and automation frameworks to support efficient deployment and testing.
• Collaborating with data scientists, analysts, and business stakeholders to understand requirements and deliver tailored solutions.
• Working with cloud services (Azure and/or AWS) to build cost-effective, high-performing data architectures.
• Troubleshooting data issues and performance bottlenecks across workflows.
• Staying up to date with emerging tools, frameworks, and industry best practices.
• Supporting data governance policies and maintaining high data quality standards.

• Proven experience as a Data Engineer in complex data environments.
• Strong working knowledge of Snowflake and dbt, including performance tuning and optimisation.
• Experience with CI/CD tools such as Git, Jenkins, or similar.
• Solid understanding of Kimball methodology and data modelling techniques.
• Familiarity with Azure or AWS services, including Azure Data Factory, Databricks, or AWS Glue.
• Proficient in SQL and Python (or Scala).
• Experience with automation testing frameworks for data pipelines.
• Databricks experience is a plus but not essential.