Analytics Engineer

at  EDF Limited

THE OPPORTUNITY…

Here at EDF, we’re looking for an Analytics Engineer to join our dynamic team. This role is ideal for a motivated individual who is passionate about building robust data products and enabling self-serve analytics within the business.
You will collaborate closely with data scientists, data engineers, and business stakeholders and will work to enable teams across the company to make data-driven decisions by providing clean, reliable, and timely data.

WHO YOU ARE

We’re looking for someone with experience in either Matillion or dbt, and Alteryx, with the proven ability to work with Snowflake or similar data platforms to build, maintain, and optimize data products – while following best practices in data management.
You’ll have excellent interpersonal skills, with the ability to influence and collaborate effectively across teams, and to explain technical concepts to non-technical stakeholders.
If you have a proactive approach and the ability to work collaboratively in a cross-functional team environment, then we’d love to hear from you!

WHAT YOU’LL BE DOING

Key responsibilities will include:

• Data Product Development: Design, build, and maintain curated data sets and dashboards, ensuring they are well-documented and enable efficient self-service to empower teams across the organisation.
• ETL Development: Build, optimize, and maintain data pipelines to ensure data is accurate, timely, and available for analysis.
• Data Integration: Utilize tools like Matillion/dbt, Alteryx and Snowflake to manage and integrate data from various sources, ensuring data consistency and availability.
• Coding and Scripting: Leverage SQL and Python to automate data processes, perform advanced data transformations, and support the development of data products.
• Documentation and Version Control: Maintain comprehensive documentation, utilize GitHub for code versioning, and ensure data cataloguing is up-to-date in Collibra.
• Performance Optimization: Monitor and improve the performance of data systems, ensuring high availability and efficiency.
• Data Quality: Implement data validation checks and monitor the quality of data pipelines, ensuring data integrity and consistency.
• Data Governance: Ensure compliance with data governance and security policies, and promote best practices in data management.