Top Data Analyst Jobs in Cologne

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.

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.

Design and build scalable HR data models (Tabular/Databricks) and Power BI Cloud dashboards to surface workforce KPIs. Collaborate with HR stakeholders to analyze hiring, attrition, retention, and headcount forecasting, identify trends, and deliver actionable recommendations to support strategic workforce planning and organizational effectiveness.

Lead delivery of end-to-end data and analytics solutions: build dimensional models, develop Power BI dashboards, perform exploratory analysis, design Azure-based data pipelines, capture client requirements, run scenario modelling, test and assure solutions, prepare client communications and lead project teams.

Lead data and analytics projects: design dimensional models, build Power BI dashboards, run exploratory analysis, capture client requirements, develop end-to-end data pipelines on Azure, test and assure solutions, support client decision-making, and contribute to business generation and people development.

Lead design and development of Tabular/Databricks data models and Power BI Cloud dashboards to support financial forecasting, cost control, and profitability analysis. Partner with Finance, Controlling, and Business Operations to perform in-depth financial and operational analyses, identify trends and deviations, and present actionable recommendations to leadership.

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.

As a Senior Data Analyst in Operations, you'll analyze data to provide insights that drive business decisions, enhance performance, and support strategic initiatives.

Lead design and development of scalable data models and Power BI reporting using Databricks, DBT and SQL. Partner with product and business stakeholders to translate requirements, drive analytics delivery, and support cloud migration to modern Azure-based analytics. Mentor colleagues, champion analytics best practices, and contribute to AI-enabled self-service capabilities.

Analyze large-scale user and product data, design and evaluate A/B tests, define and validate product and business metrics, build dashboards and reports, and partner with engineers, data scientists, product, and customers to translate insights into product improvements.

Maintain and validate global customer master data across systems (Salesforce, Jira), perform deduplication and hierarchy management, conduct compliance checks (embargo/VAT screening), monitor CMD dashboards, support cross-functional teams to resolve data anomalies, and drive data integrity projects and process improvements.

Support a UK healthcare/pharmacy client in enterprise data strategy, governance and data quality. Coordinate data backlog, establish governance and ownership models, contribute to customer data strategy, and help define reporting and analytics operating models. Prepare analysis, documentation and recommendations for programme decision-making.