ML Data Engineer

ML Data Engineer focuses on they will support the customer analytics function by developing and deploying machine learning models and techniques to deliver value around our consumer (b2c) data.

What the role involves

• They will support the customer analytics function by developing and deploying machine learning models and techniques to deliver value around our consumer (B2C) data.
• Maintain robust, scalable, and efficient data pipeline architecture to support current and future business needs.
• Processing Automation and Optimisation : Identify.
• Design, and implement improvements to automate manual processes, enhance data delivery performance, and re-architect infrastructure for improved scalability and resilience.
• AI Agentic Workflows: Designs and deploy agentic workflows to increase efficiency in support of Engineering and the Analytics function.
• AI adoption : Build on the current usage of AI across the business rolling out new processes and models.

Skills and requirements

• Relevant technical experience with the stack, platform, data, or support environment is important.

Confirmed role details

• Take the lead in building out our capability in this area with new models and recommender systems.
• Manage the infrastructure necessary for optimal ETL or ELT of data using Python , SQL , and Google Cloud Platform (GCP) big data technologies, such as BigQuery, Dataflow, Dataproc.
• Business Intelligence Enablement : Prepare and transform pipeline data to support downstream analytics and feed BI tools (DOMO), enabling data-driven decision-making across the org.
• Education & Professional Background : Holds a graduate degree in Computer Science, STEM related quantitative field, with 2+ years of hands-on experience in a data engineering role.

Candidate fit

• Leading Data Engineer will be exposed to best practice methods with the current framework.

Additional role context

• Strong SQL skills and hands-on experience with both relational and non-relational databases , supporting data needs in fast-paced, content-driven environments.
• Proven ability to automate and optimise data workflows , using modern ETL/ELT tools (e.g., Airflow, dbt, Apache Spark) to ensure timely and reliable delivery of data.
• Experience building robust data models and reporting layers to support performance dashboards, user engagement analytics, ad revenue tracking, and A/B testing frameworks.