As a Data Engineer / AI Engineer in the Operational Analytics team, you will be the hands-on builder who designs, develops, and maintains our data infrastructure from the ground up — ahead of full IT handover. This is not a Database Administrator role. We need someone who has built things from scratch, is comfortable operating in ambiguity, and is excited to shape both the engineering and AI layer of a growing analytics function.
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Write clean, well-documented Python and SQL code with a focus on performance, maintainability, and reusability.
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Architect and develop data models within our centralized data platform, ensuring data quality, consistency, and traceability.
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Build new data assets from raw sources, bridging operational systems and the analytical layer until IT takes full ownership.
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Develop and implement AI/ML-enabled data features — including data enrichment, anomaly detection, and predictive pipelines.
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Integrate AI APIs and LLM-based tools into data workflows to accelerate analytics at scale.
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Work closely with analysts and business stakeholders to understand data needs and translate them into robust engineering solutions.
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Design, build, and maintain scalable ETL/ELT pipelines that ingest, transform, and serve data across the organization.
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Support the transition of built solutions to IT once production-ready, ensuring proper documentation and handover.
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Bachelor's or Master's degree in Computer Science, Data Science, Engineering or a related field.
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3+ years of hands-on experience in data engineering, strong managing large datasets and fluent with SQL querying, including building ETL/ELT pipelines and data models from scratch.
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Strong proficiency in Python (pandas, PySpark, or similar) and SQL.
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Experience with cloud-based data platforms (e.g., Snowflake, Azure DL, AWS S3, Databricks).
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Practical knowledge of AI/ML concepts and experience integrating AI capabilities into data products or pipelines.
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Excellent communication skills: able to engage with business stakeholders and translate requirements into technical designs.
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Comfortable working as a builder and owner before IT infrastructure is fully in place — a 'zero-to-one' mindset.
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Familiarity with orchestration tools such as Apache Airflow, dbt, or Azure Data Factory.
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Exposure to LLM frameworks (LangChain, OpenAI API, Azure OpenAI).Experience with version control (Git/GitHub) and CI/CD practices for data pipelines.
Bracknell
Bracknell Forest
United Kingdom
