Data engineer in GenAI

Описание

Digital Skills are working with a Global Technology Business developing GenAI applications across areas including search and discovery, conversational AI, customer support and LLM evaluation. They require a Data Analytics Engineer to turn complex application data into reliable analytical datasets, insights and monitoring capabilities.

Задачи

• Own analytical and application data domains end-to-end.
• Develop scalable analytical data models and reusable data products.
• Combine, transform, clean and enrich large datasets from multiple sources.
• Investigate GenAI application data to understand what is working effectively and where problems or user pain points occur.
• Develop application monitoring, data quality and cost dashboards.
• Build reusable datasets that enable self-service analytics.
• Analyse experiments, model behaviour and LLM evaluation results.
• Perform exploratory analysis and visualisation to validate data and GenAI products.
• Maintain data quality, consistency and analytical logging.
• Monitor and troubleshoot analytical data pipelines.
• Work with product managers, engineers and data scientists to identify analytical opportunities.
• Support data governance, classification, quality, compliance and security requirements.

Требования

• Strong SQL skills and experience working with relational and analytical databases.
• Strong Python skills, with Python specifically highlighted by the hiring manager as important for transformational data and the GenAI environment.
• Hands-on experience with PySpark.
• Strong data modelling and modern data warehouse experience.
• Experience manipulating and processing large, complex datasets at scale.
• Strong analytics, BI and business problem-solving capabilities.
• Ability to translate business or product questions into meaningful metrics, datasets, analysis and visualisations.
• Experience owning analytical domains end-to-end, including data quality, modelling, monitoring and stakeholder-facing analytics.
• Experience creating maintainable, reusable, production-grade analytical transformations.
• Comfortable working with free text, unstructured datasets, AI-generated outputs and application telemetry.
• Excellent stakeholder communication and the ability to explain technical findings to non-technical audiences.
• Self-driven and comfortable independently identifying where analytics can create value.
• The ideal candidate will have 3+ years' experience and a background closer to an Analytics Engineer or highly technical Data Analyst than a traditional infrastructure-focused Data Engineer.
• Будет плюсом: Experience with experimentation, model analysis, AI/ML products or LLM evaluation would be highly advantageous; Exposure to dbt, Snowflake, Streamlit, Airflow or Argo would be advantageous; Ability to understand Java would be beneficial, although the hiring manager confirmed that writing Java is not essential.