Top Data Analyst Jobs in Amsterdam

Supports commercial reporting and business decision-making by developing Power BI dashboards, automating reporting processes, defining KPIs, improving data quality, and maintaining governance documentation. The role collaborates with international stakeholders, IT teams, and analytics colleagues on data solutions and projects. Responsibilities include requirements gathering, data validation, process improvement, user training, and documentation. This is a 12–15 month maternity-cover position.

Supports commercial reporting, analytics, and business decision-making across international stakeholders. Develops and maintains Power BI dashboards, automates recurring reporting with Microsoft tools, standardizes KPI definitions, investigates data discrepancies, and contributes to data governance and quality initiatives. The role also supports analytics projects, documents data solutions, validates reporting, and provides training and guidance to business users.

Analyze marketplace data to identify growth opportunities, define metrics and OKR targets, forecast performance, build dashboards, support strategic decision-making, and automate recurring reporting workflows. The role partners with stakeholders across strategic initiatives and translates complex analysis into actionable business insights.

Work with Product and Insurance teams to define KPIs, analyze A/B tests, build and maintain real-time dashboards, contribute to dbt-based data pipelines, and provide data-driven recommendations to influence product roadmap and outcomes.

As a Data Analyst, you'll advise clients on data management strategies, implement solutions, and enhance data governance and quality. You'll also provide insights for business decisions based on analytical data evaluations.

The Digital Data Analyst ensures accurate data tracking, analyzes performance across digital channels, and reports insights to drive marketing decisions.

The FP&A Data Analyst will enhance financial reporting, automate processes, bridge data across systems, and develop insightful dashboards while collaborating with finance and engineering teams.

Own end-to-end development of business-critical dashboards and reports, analyze data from SQL and NoSQL sources, and deliver executive-ready insights. Partner with Data Engineering on data quality, pipelines, and models; monitor KPIs across programs, funnels, and revenue; identify trends and bottlenecks; mentor junior analysts; and promote self-service reporting across the organization.

Perform data analysis across growth revenue management, marketing, customer lifecycle, CRM, and risk analytics. Use statistical and programming tools to acquire, clean, integrate, analyze, interpret, and visualize data. Develop hypotheses and actionable recommendations, present findings, facilitate workshops, manage executive stakeholders, and mentor team members.

Perform data analysis across revenue management, marketing, CRM, and risk analytics. Use statistical and programming tools to acquire, clean, integrate, analyze, and visualize data; develop hypotheses and actionable recommendations; present findings to stakeholders and executives; facilitate workshops; and mentor team members.

Supports Oliver Wyman’s Data and Analytics consulting team by designing analytical solutions, developing prototypes, applying machine learning and AI techniques, validating models, and assessing their effectiveness. The role involves collaborating with consultants and clients, explaining complex data science concepts clearly, and contributing to projects across financial services, sustainability, retail, transportation, and other industries. International travel and continuous technology development are expected.

Develop analytical solutions for consulting clients across financial services, sustainability, energy, retail, and transportation. Responsibilities include designing and prototyping data science solutions, applying machine learning and advanced analytics, validating models and GenAI applications, and communicating complex findings to clients and colleagues. The role involves collaboration across global teams, continuous learning, and potential international travel.

Collaborate with consultants and clients to design, prototype, validate, and implement data and analytics solutions. Projects may involve NLP, machine learning, GenAI validation, credit risk modeling, decarbonization, promotion optimization, supply chain risk, and customer churn. Explain sophisticated analytical concepts clearly, contribute to client discussions, and develop quantitative problem-solving skills in a collaborative consulting environment.

Develop analytical and machine learning solutions for global consulting clients, including prototypes, predictive models, NLP, GenAI validation, risk analytics, and optimization tools. Collaborate with clients and colleagues to explain sophisticated data science concepts, assess solution effectiveness, and deliver impactful results across industries such as financial services, energy, retail, and transportation.

Lead data extraction, validation, and migration efforts using advanced SQL and Azure data services. Perform data mapping, reconciliation, quality assessments, and root-cause analysis. Collaborate with stakeholders, data engineers, and project teams to translate requirements, support SIT/UAT, optimize SQL scripts, and maintain technical documentation to ensure data integrity and compliance across migration lifecycles.

Analyze large datasets to identify trends and deliver actionable insights; design and monitor KPIs; build and maintain dashboards and reporting frameworks; conduct ad hoc analyses; ensure data quality and support reporting infrastructure in partnership with product and business teams.

Early-career Data Analyst responsible for designing and owning core metrics and dashboards, analyzing transaction/customer/credit data to inform credit, fraud, AML, portfolio and growth decisions, building data pipelines and automations with SQL and Python, partnering with cross-functional teams, and owning compliance reporting. Emphasis on turning ambiguous problems into reliable analytical systems and applying GenAI/LLMs for practical automation.

Analyze and instrument offsite discovery and SEO/GEO performance for ecommerce sites. Build integrations, ETL pipelines, and models to track organic and AI-driven traffic. Create tooling and frameworks for technical site audits, landing page modelling, SEO experiments, and enablement for product and customer teams.

Own supply-side analytics: define and monitor partner KPIs, build self-serve dashboards and LLM-powered tools, run experiments and A/B tests, create business cases for commercial decisions, support provider integrations, and raise analytics standards across the team.

Lead end-to-end analytics projects for hardware infrastructure automation: design data models and pipelines, build dashboards, analyse large-scale telemetry, quantify automation impact, and present actionable recommendations to engineering stakeholders. Ensure data quality, documentation, and mentor colleagues to improve team standards.

Partner with Marketing, Sales, Finance, Product and other teams to turn business questions into actionable analysis. Own end-to-end analytics: build datasets and dashboards (dbt, LookML), maintain metric definitions and caveats, use and curate agent tooling for trustworthy answers, and contribute small data platform and pipeline improvements.

As a Senior Analyst in the Data & Analytics team, you will develop analytics solutions, engage in client discussions, and support projects across industries.

Develop data-as-a-product solutions and Power BI reports, engage stakeholders to gather requirements, build scalable self-service dashboards, ensure data quality and ethical sharing, train users, and support reporting rollouts for supply-chain and sustainability insights.

Work on client-facing analytics projects: design solutions, build prototypes, implement ML/NLP models (e.g., PD models, decarbonization, churn, GenAI chatbots), validate results, and communicate findings to clients and colleagues across industries.

Lead and support credit risk data governance initiatives: manage metadata, catalogs, and business glossaries; monitor and remediate data quality; analyze SQL datasets and data warehouses; document standards; coordinate stakeholders; and lead small teams to deliver governance solutions within banking/financial services.