Key Skills

Job Description

Data Analytics Engineer (SQL, Python, Data Modelling & GenAI Analytics) €100 per hour – Based on a 40-hour working week 6-month contract Amsterdam (Hybrid) – 1 to 2 days per week onsite About the Client 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. About the Role This is an analytics-led position rather than a traditional Data Engineering role. The successful candidate will combine strong SQL, Python and data modelling skills with a commercial and product-focused mindset, using data to understand how GenAI applications are performing and identify opportunities for improvement. This role sits between Data Engineering and BI or Analytics, but leans more towards analytics and BI. You should have enough engineering capability to combine and transform large datasets, while being able to understand business requirements, identify meaningful insights and visualise application performance. Responsibilities 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 Desired Skills and Experience 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 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 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 Summary of the Best Candidate 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. You will combine strong SQL, Python and data modelling skills with excellent analytical and commercial thinking. You should be comfortable independently exploring complex datasets, identifying meaningful insights and using data to understand and improve GenAI applications. This role is less suited to candidates whose experience is primarily focused on data infrastructure, platforms or architecture.

Core Responsibilities

Own analytical data domains and develop scalable, reusable data models and products by transforming and enriching large datasets. Analyze GenAI application performance, experiments, and LLM evaluation results, while building monitoring and data quality dashboards and collaborating with product, engineering, and data science teams.

Requirements

The role requires 3+ years of experience, strong SQL and Python skills, hands-on PySpark experience, and expertise in data modelling, modern data warehouses, and large-scale datasets. Candidates should combine technical analytics capabilities with business-focused problem-solving, stakeholder communication, and experience delivering reliable, reusable analytical transformations.