Data Analytics Engineer

Salt is currently hiring a Data Analytics Engineer for a client of ours in Amsterdam.

Role is freelance(kvk/zzp) paying 100 euros an hour.

We are looking for a Data Analytics Engineer II to support GenAI applications across Search & Discovery, AI Companions and Chatbots, Customer Support AI, and LLM Evaluation.

This is an analytics-first, product-oriented role focused on transforming complex data into actionable insights, defining meaningful metrics, evaluating AI application performance, and enabling data-driven product decisions.

You will own analytical domains end-to-end, combining strong SQL, Python, and data modeling skills with business acumen, analytical thinking, and cross-functional collaboration. This role is closer to an Analytics Engineer or highly technical Analyst than a traditional Data Engineer.

Key Responsibilities

• Own analytical data domains end-to-end, ensuring data quality, consistency, and usability.
• Design and maintain scalable data models, reusable analytical datasets, and production-grade transformations.
• Analyze large datasets to identify trends, investigate issues, define KPIs, and generate actionable business and product insights.
• Build dashboards and monitoring frameworks for application performance, quality, usage, and cost.
• Analyze experiments, model behavior, LLM evaluation results, and AI application failure patterns.
• Work with structured and unstructured data, including application telemetry, free text, and LLM-generated outputs.
• Partner with Product Managers, Data Scientists, and Engineers to define metrics, validate features, and improve product outcomes.
• Monitor data quality and pipeline health, proactively resolving issues and optimizing performance.

Qualifications & Skills

• 3+ years of experience in Analytics Engineering, Data Analytics, Product Analytics, or a related technical role.
• Strong business and product mindset, with the ability to translate ambiguous problems into measurable outcomes.
• Strong SQL skills and hands-on experience with Python and PySpark.
• Solid understanding of data modeling, modern data warehouse practices, and production-grade analytical transformations.
• Experience with exploratory analysis, metric definition, dashboarding, experimentation, and data quality monitoring.
• Ability to work independently, manage ambiguity, and communicate insights effectively to stakeholders.
• Experience with AI/ML products, LLM evaluation, or model/error analysis is an advantage.
• Familiarity with dbt, Snowflake, Streamlit, Airflow, or Argo is a plus.