Data Analytics Engineer

Data Analytics Engineer | SQL, Python/PySpark, Data Modelling

Up to €110 per hour (based on a 40-hour week)

Hybrid working- 2 days per week onsite in Amsterdam, Netherlands

Initial contract until 31st December 2026

About the Client

Digital Skills are working with a global technology business at the forefront of AI innovation. The organisation is delivering cutting-edge generative AI applications across multiple products, focused on improving customer experiences through data-driven insights and intelligent systems.

About the Role

This is an exciting opportunity for a Data Analytics Engineer to join GenAI application teams supporting products across search, conversational AI, and customer support. Sitting at the intersection of analytics, product, and engineering, you'll transform complex data into actionable insights that directly influence product performance and business decisions.

This is not a traditional Data Engineering role focused on building data platforms or infrastructure. Instead, we're looking for someone with a strong analytical and product mindset who enjoys solving business problems through data, defining meaningful metrics, and delivering insights that improve AI-powered products.

You'll own analytical domains end-to-end, partnering closely with Product Managers, Data Scientists, and Software Engineers to ensure data is accurate, well-modelled, measurable, and actionable.

Responsibilities

• Own analytical data domains from end to end, ensuring data quality, accuracy, consistency, and reliability
• Design and develop scalable analytical data models and reusable datasets that support business decision-making
• Transform large, complex datasets into actionable insights that influence product strategy and customer experience
• Define and track meaningful product metrics, KPIs, and success measures for AI applications
• Build dashboards and monitoring solutions covering product performance, model quality, operational health, and cost
• Analyse A/B tests, experiments, model performance, and LLM evaluation outputs to generate business insights
• Conduct ad hoc investigations and proactively identify optimisation opportunities through data
• Collaborate with Product, Data Science, and Engineering teams to define analytical solutions for complex business challenges
• Work with structured and unstructured datasets, including text, LLM outputs, and telemetry data
• Maintain the health and performance of analytical datasets and pipelines through monitoring and optimisation

Desired Skills and Experience

• Expert-level SQL skills within analytical or large-scale data environments
• Strong Python and/or PySpark experience for data manipulation and large-scale analysis
• Excellent data modelling skills and understanding of modern analytical data warehouse practices
• Proven experience owning analytical domains from data modelling through to reporting, experimentation, and stakeholder insights
• Strong product mindset with the ability to define metrics, answer business questions, and influence product decisions through data
• Experience analysing experiments, customer behaviour, or product performance in a product-focused environment
• Comfortable working with machine learning or AI-related datasets, including model evaluation and error analysis
• Experience working with unstructured or free-text data is highly advantageous
• Familiarity with tools such as dbt, Snowflake, Streamlit, or Airflow is beneficial
• Excellent communication skills with experience presenting analytical findings to technical and non-technical stakeholders

Summary of the Best Candidate

The ideal candidate is an Analytics Engineer or highly technical Data Analyst rather than a traditional Data Engineer. You'll combine strong technical skills with commercial awareness, using data to answer product questions, define meaningful metrics, and drive measurable business outcomes.

Successful candidates will typically have experience owning analytics end-to-end, including:

• Data modelling and dataset design
• Data quality and governance
• Dashboarding and monitoring
• Experimentation and performance analysis
• Stakeholder-facing analytics and business problem solving

While strong technical skills are essential, approximately two-thirds of the role focuses on analytics, product thinking, and business impact, with the remaining one-third centred on data engineering and data modelling. Candidates should therefore be equally comfortable discussing technical implementation and explaining how their work influenced product or business decisions.