Top Data Analyst Jobs in Germany

Apply data analytics, machine learning, and AI to production activities; identify digitalization use cases; deploy AI solutions on OpenShift; develop business reports; and replace Excel-based tools with automated solutions using Palantir Foundry, OpenShift AI, and TIBCO Spotfire.

Owns business and performance analysis for B2B2C products, partners, and markets. Analyzes large datasets, builds commercial and operational reports, identifies trends and opportunities, and translates findings into recommendations for senior stakeholders. Supports pricing analysis, partner reviews, benchmarking, and business cases while collaborating with Product, Sales, Finance, and Operations. Ensures reporting accuracy and consistent interpretation across dashboards and reports.

Transforms graphene photonics characterization data into insights for device design, process development, and manufacturing. Owns analysis software and scalable data infrastructure, develops methods for evolving experiments, ensures data integrity and traceability, extracts device parameters, performs statistical and yield analyses, correlates measurements with process parameters, and integrates outputs with fab MES systems for real-time process feedback. Requires strong Python development, device physics knowledge, model fitting, visualization, and laboratory or fab instrument experience.

Provides independent technical data analyst support for the M2i raw materials intelligence platform. Responsibilities include designing data models and analytical datasets, integrating and validating data sources, developing and optimizing Power BI reports and Microsoft Fabric components, improving refresh performance, implementing access management and usage monitoring, performing quality assurance, documenting technical solutions, and preparing platform enhancements for future releases.

Collect, clean, and analyze large datasets to measure digital campaign performance, identify trends and causal impact, run experiments, build predictive models, and create visualizations and dashboards to inform business decisions.

Lead design and delivery of scalable analytics solutions using dbt, SQL, Databricks and Microsoft Fabric. Build Power BI dashboards and semantic models, collaborate with product and business stakeholders, modernise cloud analytics, mentor analysts, and promote analytics best practices including AI-assisted capabilities.

The Staff Data Analyst in Marketing Analytics develops statistical models, performs quantitative analysis, proposes marketing strategies, and mentors others while collaborating with marketing and data engineering teams.

The Data Analyst will own analytics projects, develop customer-facing data products, conduct quantitative analysis, and drive decision-making through data insights.

As a Data Analyst, analyze user behavior and product performance to create reports and dashboards, collaborating with teams for data-driven insights.

Analyze product data to identify opportunities, evaluate feature performance, and guide data-driven product decisions. Partner with product squads, engineers, designers, and researchers to define requirements, build analyses and dashboards, maintain ETLs, and evaluate A/B tests. Communicate actionable insights and recommendations to stakeholders and senior management throughout the product lifecycle.

Work with product squads to discover opportunities, run analyses and A/B tests, own end-to-end investigations, create dashboards, and recommend data-driven product decisions to stakeholders and senior management.

Own B2C marketing KPIs and analyze acquisition funnels, attribution, conversion, trials, and churn. Build reporting frameworks, self-serve data products, and AI-powered insights; improve tracking, measurement, compliance, and advertising-platform data activation. Partner cross-functionally with Marketing, Product, and Data teams to translate complex analyses into actionable growth, budget, channel, and acquisition recommendations.

Analyze and govern commercial data across Finance, Pricing, and Revenue Operations. Build SQL and dbt data pipelines, consumption models, audit-ready data products, and Looker Studio dashboards. Reconcile and validate complex datasets, translate business logic into reliable metrics, and communicate actionable insights to technical and non-technical stakeholders. The role requires close collaboration with globally distributed teams in a hybrid Berlin office environment.

Analyze, validate, and interpret business, customer, and product data to generate actionable insights. Build and maintain Power BI dashboards, reports, and KPI solutions; monitor data pipelines; support data quality and governance; and maintain data catalogs and KPI definitions. Collaborate with stakeholders through Jira workflows, enable self-service analytics, and communicate findings clearly. The role requires SQL, PySpark or Python, Excel, Power BI, statistical knowledge, German and English proficiency, and openness to AI-driven analytics.

Perform deep operational data analysis, build queries and dashboards, automate ETL and data processes, develop monitoring tools, present insights and recommendations to management, and collaborate with squads using SQL, Python/R and visualization tools.

Lead operational analyses and present data-driven recommendations. Query data lakes with SQL, perform deep-dive analysis in Python/R, automate ETL and monitoring, build dashboards (Metabase), and communicate insights to influence business decisions.

Build end-to-end compliance analytics and data infrastructure for KYC/KYB funnels, remediation campaigns, and financial exposure. Model and transform data with dbt and Omni, create self-service dashboards for product, ops, risk and compliance, and measure campaign impact to drive operational decisions.

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.

Support multilingual data quality by analyzing and cleaning text data, developing parsers, evaluating text classifiers, translating taxonomies (Skills, Titles, Occupations), and partnering with Linguistics and Engineering to deliver high-quality global data.

Transform cloud, AI, SaaS, and on‑prem billing data into forecasts, dashboards, and optimisation recommendations. Build cost models, KPIs, automate pipelines and reporting, validate forecasting tools, drive tagging and allocation strategies, and advise clients through workshops and consulting engagements.

Analyze and process tumor-derived NGS datasets (RNA‑Seq, WES, WGS, single‑cell), develop and apply bioinformatics pipelines, interpret cancer genomics results, collaborate with multidisciplinary teams, and contribute to diagnostic solution development.

Lead and grow the RevOrg analytics team, own revenue KPIs, reporting, forecasting, attribution, and data governance. Align GTM functions on unified metrics, drive data quality and scalable analytics frameworks, and deliver executive-ready insights to support predictable SaaS revenue growth.

Lead end-to-end revenue analytics and forecasting, build lifecycle dashboards, develop multi-touch attribution, measure marketing and sales productivity, and drive data quality and governance across CRM and GTM systems.