DAX Experts in Berlin
for trusted data models, matched in minutes with vetted and available freelancersHire experts who create reliable Power BI semantic models, optimize DAX measures and connect business logic across Microsoft Fabric, Excel and SQL Server Analysis Services. FRATCH matches you quickly and precisely with vetted, available freelancers.
Meet FRATCH Experts in Berlin, who have recently used DAX
William N.
Last position:
Power BI Solutions Architect/Engineer & AI Consultant at AVERDUNG GmbH
- Redesign of the company's BI infrastructure: replacement of a fragmented landscape of manually maintained Excel solutions and CSV imports with a centralized Power BI environment featuring a unified data model as the company-wide single source of truth
- Consolidation of previously isolated reporting logic into a central semantic model – eliminating redundant files, manual data transfers, and inconsistent metrics between departments
- Forecasting & planning: Design and implementation of company-wide liquidity planning in Power BI – from business logic to a fully automated, data-source-driven planning model replacing the previous manual Excel process; enables rolling forecasts and continuously up-to-date cash flow transparency for management
- Optimization of existing Power BI dashboards in terms of performance, structure, and analytical value using an AI-native approach
- Analysis and improvement of the data model, including data quality analyses, data cleansing, and consistent modeling using star schema, DAX, and Power Query
- Incident & anomaly analysis: Identification, investigation, and explanation of data anomalies, including root-cause analysis and concrete recommendations for action
- AI solution architecture: Connecting Business Central and Power BI to LangDock via MCP (Model Context Protocol) for AI-supported data usage
- Creation of a historical data layer as a basis for trend and time-series analyses
- AI-supported automation: Design and development of AI skills, agents, loops, and processes for the automated analysis and interpretation of reports
- Automated reporting workflow: Setup of scheduled, automated email distribution of AI-generated analyses and recommendations to stakeholders
- Gathering and documentation of business requirements and coordination with business departments and IT as part of requirements engineering / product owner activities
- Breaking down overall requirements into clearly defined work packages and tasks
- Definition, prioritization, and management of milestones throughout the entire project lifecycle
Tools: POWER BI, M365, Copilot Studio, MIRO, Microsoft Business Central, Microsoft Fabric, Claude AI, ChatGPT, LangDock, MS VS Code
Stefan O.
Last position:
Founder at ProtocolEngine.io
Evidence-led health intelligence platform turning published research into personal health protocols. It scores 430 habits, foods, and supplements against the studies behind them, and moves the score when the evidence moves. Built solo.
- Built the daily ingestion pipeline across PubMed, bioRxiv, and medRxiv: 43,000+ papers from 3,400+ journals processed into 230,000+ typed evidence claims, each one traceable back to the study it came from.
- Designed the six-factor evidence scoring model and the public changelog behind it, so no recommendation ever appears without the papers underneath it. 23,000+ grade changes recorded and explained to date.
- Shipped an entity information model connecting every intervention to its mechanisms, biomarkers, and outcomes: 118 biomarkers with region-specific reference ranges, 77 mechanisms, 32 graded outcomes.
- Built the personalisation layer: blood panel ingestion that reads lab PDFs with a vision model and corrects results for draw time against the user's wake anchor, plus Oura, WHOOP, and Withings integration for daily readiness context.
- Operate eleven specialised review agents over the corpus and codebase, covering paper curation, retrieval quality, health-claim compliance across EU and US regimes, and security.
- Shipped the Evidence Assistant, a RAG assistant that answers from the claim database and cites the underlying papers, plus a B2B practitioner tier, an Expo React Native app, and localisation across 3 languages and 7 markets.
Stack: Next.js 16, TypeScript, Supabase, pgvector, Anthropic Claude, Vercel, DeepInfra.
Nisanthan S.
Last position:
Business Intelligence Consultant (freelance) at NBIC – Nisanthan BI Consulting
Advising companies on building, migrating and optimising BI and reporting landscapes (Power BI, SQL, Python, ETL)
5 client engagements in real estate and finance since 05/2025: taking over and stabilising existing reporting, automating recurring standard and management reports, building cash-flow models
Proposal and feasibility assessments for BI and reporting projects
Using AI-assisted development (Claude Code) to accelerate automation, tooling and web/app development
Custom ERP system
Problem: A client's core processes ran on scattered, siloed Excel files with no central data storage – error-prone, hard to scale and impossible to analyse end-to-end.
Approach: Captured the business processes and requirements, modelled the data and developed iteratively together with the business team.
Implementation: Built a tailored, web-based ERP system with a central database, role-based modules and automated reporting – delivered using AI-assisted development in Claude Code.
Timesheet app
Starting point: Time tracking based on an overgrown, macro-heavy Excel template – maintenance-intensive, single-user and error-prone.
Implementation: Migrated all functionality and VBA macros into a standalone web app with central data storage, multi-user support and automated reporting.
Cash-flow modelling
Starting point: The existing cash-flow model covered standing investments only; project developments were missing from steering.
Implementation: Built and extended the CF model to include project-development cash flows.
Optimisation: Reviewed and optimised existing CF models and expanded the KPI outputs for reporting and steering.
Anshita S.
Last position:
Business Intelligence Developer and Data Analyst at Deloitte Consulting
Specialize in turning complex data from diverse environments into actionable business value through compelling visual storytelling. I am an expert in generating actionable insights and presenting recommendations to business stakeholders. My technical proficiency in SQL, Python, and leading data visualization tools like Tableau and Power BI allows me to deliver a new generation of self-service tools and analytics services.
- Data Visualization & Storytelling: Created impactful data visualizations and dashboards in Tableau and Power BI, effectively communicating findings and presenting actionable recommendations to C-suite stakeholders and business leaders.
- Stakeholder Management: Built effective working relationships with key business stakeholders, data engineers, and other partners to achieve common data-driven goals and targets.
- Insights & Recommendations: Generated actionable insights from complex data analysis for funnel conversion, marketing performance, and ROI, directly influencing business performance and strategy.
- Data Collaboration & Empowerment: Worked closely with cross-functional teams to support the ongoing data needs of internal partners, helping to optimize internal data processes and workflows.
- BI & Data Expertise: Applied extensive experience in data modeling, data collection, data mining, and analysis to deliver end-to-end analytical solutions from stakeholder discovery to production.
Tobias L.
Last position:
Data Engineer at unitb consulting GmbH
Tasks: Design and operation of end-to-end cloud data platforms for enterprise clients in publishing and finance, including infrastructure automation, pipeline development, monitoring, and data quality.
Activities:
- Built multi-layer data architectures on Databricks (Apache Spark, Delta Lake), BigQuery, and GCP
- Fully automated cloud infrastructure with Terraform across 3 environments (DEV/STG/PRD)
- Developed automated data pipelines with Python, dbt, and GCP services for different data sources
- Built monitoring and alerting systems for real-time platform monitoring
- Implemented data versioning and quality checks at every layer
- Designed automated test and deployment pipelines in GitLab and Bitbucket
Achievements:
- 2× production data processing capacity, reduced spike response time from minutes to ≤15 s, server errors ≈ 0
- Replaced 3,000 lines of manual configuration with a reusable automation module for 7 customer domains, configuration errors to 0
- Delivered a complete end-to-end data platform at ~€10/month infrastructure cost
- Migrated 7 database tables with 0 downstream issues
- Removed 100% exposed credentials, eliminated external vendor dependency
- Delivered integration of 3 teams in 1 sprint
Noël L.
Last position:
Freelance Senior Product Owner at Rewe Digital GmbH
- Interim Product Owner for two new applications as part of the introduction of a new bonus points program within the entire Rewe Group
- Coordination of cross-team planning (initiative management) and implementation
- Responsibility for product vision, roadmap, backlog and stakeholder management within an agile scaled product development team
- Successful Go Live with both applications on 2024-09-24
Mojtaba P.
Last position:
Head of Data Analytics & BI at Urlaubstracker GmbH
- Owned the analytics stack end-to-end across data modeling, cloud setup, access control, cost management, and stakeholder-facing dashboards.
- Built and maintained large-scale data workflows across 20+ APIs and 100M+ rows using GCP, BigQuery, dbt, and Spark.
- Supported product, marketing, finance, and commercial teams with KPI frameworks, reporting layers, and decision support.
- Introduced automation and AI-assisted analytics use cases to improve insight generation and internal workflows.
Vili D.
Last position:
Technical Lead, Data Engineer at Mercedes-Benz Consulting
- Optimized the data architecture (medallion) to better decouple processing stages and improve transparency and reproducibility
- Ensured technical quality of data processing in Databricks by introducing schema enforcement, data quality checks and a structured data architecture
- Orchestrated pipelines with Azure Data Factory
- Professionalized and automated the development and deployment process by integrating Git and GitHub Actions
- Led the Data Engineering team (3 members) in a functional role
- Conducted workshops to optimize and stabilize the data platform and the development process
- Collected and prioritized new requests, maintained the product backlog
- Technologies: Microsoft Azure (Data Lake, Data Factory), Databricks, Apache Spark (PySpark), Python, SQL, Git, Confluence, Power BI, Power Apps, Dataverse, MS SharePoint, Mural
Lasya M.
Last position:
Data Engineer at Carelon Global Solutions (Elevance Health)
- Designed and implemented scalable ETL/ELT pipelines using Python, SQL, dbt, AWS and Informatica to ingest data from sources such as APIs, relational databases, and flat files into Snowflake, reducing pipeline runtime by ~30%.
- Migrated high-volume datasets from on-premises Teradata to Snowflake using AWS services (S3, Glue, Step Functions, IAM), ensuring data consistency and integrity.
- Applied Kimball methodology to design star and snowflake schemas, improving query performance and reducing Snowflake compute costs.
- Implemented automated data quality checks using SQL-based dbt tests and the Great Expectations framework to detect anomalies and enforce data correctness before production loads.
- Orchestrated ETL workflows in Airflow using Python and managed code deployments via Git with CI/CD best practices to increase deployment reliability and maintain pipeline uptime.
- Built interactive Power BI dashboards and curated datasets to enable data-driven decision-making for stakeholders.
- Maintained technical documentation in Confluence for ETL workflows, and led knowledge-sharing sessions for new joiners.
Ola A.
Last position:
Freelance BI Consultant & Analyst at DocMorris
- Data / product analysis & visualisation in Tableau Desktop
- Stakeholder Management
Deependra P.
Last position:
Data Specialist at Cloud Factory
- As a Data Specialist, I leveraged analytical expertise to transform raw data into actionable insights, driving strategic decision-making and operational improvements. My role encompassed data interpretation, reporting automation, and cross-functional collaboration, utilizing advanced tools such as Microsoft Excel, Power BI, and Python for comprehensive data analysis.
- Implemented Python scripts to validate and reconcile large datasets, reducing manual errors and improving data reliability.
- Utilized Python (Pandas, NumPy, Matplotlib/Seaborn) to automate data cleaning, analysis, and visualization, improving efficiency and accuracy in reporting.
- Developed interactive dashboards in Power BI to present key metrics, trends, and performance indicators, facilitating real-time decision-making.
- Designed and executed automated reports using Excel (Pivot Tables, Power Query, VBA) and Power BI, ensuring data accuracy and consistency across departments.
- Data Analysis: Excel (Advanced Pivot Tables, Power Query), Power BI (DAX, Data Modeling), Python (Pandas, NumPy, Visualization Libraries)
- Automation & Reporting: Power BI Dashboards, Excel Macros (VBA), Python Scripting.
Germo G.
Last position:
BI Developer at Rhenus Logistics
- Business intelligence development in the logistics sector.
- Migrating existing reporting and analysis solutions from Cognos to Microsoft BI.
- Using Microsoft SQL Server, SSAS, Power BI, and other Microsoft BI platform components.
Tarek S.
Last position:
Cloud Engineering at Freelancer
- Development, operation and support of BI platforms and microservice architectures
- Development of an open-source BI scheduling tool
Arne E.
Last position:
Co-Founder Business Analyst & Strategic Consultant at GOOD FOR GmbH
- Launched new startup
- Improving dashboarding for all departments
- Ran successful pilot
- Ownership business case
- Verbally committed pre-seed investment
Discover over 15,000 top freelancers
Statistics of experts using DAX
Aggregated from the professional profiles of matched freelancers.
Experience
12 years (Germany: 16 years)
Position duration
1.9 years (Germany: 2.9 years)
Positions per freelancer
10 (Germany: 12)
Top business areas
Business Intelligence, Information Technology, Project Management
Top industries
Information Technology, Banking and Finance, Professional Services
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
100% (Germany: 97%)
Master's degree or higher
62% (Germany: 71%)
Doctorate
8% (Germany: 9%)
Certifications per freelancer
3
Most common languages
German, English, Spanish
Speak two or more languages
100% (Germany: 96%)
Based on our profile pool as of 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Berlin are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Berlin using DAX
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
DAX experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Information Technology (93%)
- Banking and Finance (57%)
- Professional Services (43%)
- Automotive (29%)
- Healthcare (29%)
- Retail (29%)
- Transportation (21%)
- Manufacturing (21%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What DAX does
DAX, short for Data Analysis Expressions, is the formula language behind analytical models in Power BI, Power Pivot and SQL Server Analysis Services. It calculates measures, calculated columns and tables while responding to filters, dates and relationships. Companies use it to turn structured business data into interactive reports and governed metrics.
Core capabilities
DAX experts define business logic that remains consistent across dashboards and reports. They work with row context, filter context, context transition, variables and evaluation behavior to produce accurate results. Strong practice also includes time intelligence, ranking, segmentation, virtual tables and reusable calculation patterns.
Ecosystem and tooling
DAX sits within the Microsoft data ecosystem rather than operating as a standalone reporting tool. Specialists commonly work with Power BI Desktop and Service, Power Pivot in Excel, Microsoft Fabric and SQL Server Analysis Services. They may also use Power Query and M for preparation, SQL for source queries, Tabular Editor for model management and DAX Studio for query plans and performance analysis.
- Build star schemas and semantic models
- Create measures for finance, sales and operations
- Review filter behavior and calculation logic
- Diagnose slow visuals with DAX Studio
When companies need support
Freelance DAX expertise helps when a reporting model has grown difficult to maintain, when figures differ between reports or when a new data product needs clear metrics. It is also useful during migrations from Excel or legacy Analysis Services models, Fabric adoption and Power BI governance work. Berlin teams can combine on-site workshops with remote delivery when domain knowledge and secure access are arranged.
- Standardize KPI definitions across departments
- Refactor complex measures and calculated columns
- Improve model and report performance
- Document logic for internal teams
What strong specialists bring
Quality work begins with the model, not with clever formulas. Strong professionals understand dimensional design, relationships, filter propagation and the distinction between storage-engine and formula-engine work. They validate results against business rules, explain trade-offs clearly and leave measures that other analysts can safely extend.
Choosing the right expertise
Look for evidence of production Power BI or tabular-model work, not only familiarity with formula syntax. Ask how the specialist tests totals, handles inactive relationships, manages date tables and investigates slow queries. For Berlin-based collaboration, clarify German or English communication needs, access constraints and whether workshops must happen on site. A practical review of a representative model or measure is often more revealing than a list of tools.
Frequently asked questions
Key details about DAX, drawn from the questions we get asked most.
DAX is used to define calculations in Power BI, Power Pivot and SQL Server Analysis Services tabular models. It supports measures, calculated columns, time-based analysis, rankings and business rules that respond to report filters.
DAX calculates within a semantic model and is designed for interactive analysis with relationships and filter context. SQL is usually better suited to extracting, joining and transforming source data, so many projects use both rather than treating them as direct substitutes.
A strong DAX specialist usually understands dimensional modeling, Power BI, Power Query and M, SQL, data visualization and governance. Experience with Tabular Editor, DAX Studio, Microsoft Fabric or Analysis Services is valuable when models need structured maintenance and performance work.
Complex DAX work requires practical experience with production tabular models, not only knowledge of formula syntax. The right level depends on the data model, calculation complexity, performance needs and the quality of existing documentation.
Yes, DAX work is often suitable for remote collaboration through shared model files, controlled workspaces and documented review processes. On-site sessions in Berlin can still help with requirements workshops, stakeholder alignment or access procedures for sensitive data.
Review whether DAX measures return correct results under different filters, totals and relationship states. A capable specialist can explain the evaluation context, test edge cases, inspect query performance and keep the model understandable for future users.
DAX is strongly associated with Power BI, but it is also used in Power Pivot for Excel and tabular models in SQL Server Analysis Services. The same language can support related analytical models, although available features and deployment practices depend on the host product.
Before starting DAX work, clarify the source systems, semantic model design, required KPIs, security rules and acceptance tests. It is also important to agree on access, documentation, deployment ownership and whether the work covers Power Query, SQL or only the analytical model.
The average hourly rate of freelancers in Berlin, Germany who have used DAX in their recent projects is 90 €, which corresponds to a daily rate of about 716 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used DAX in their recent projects, 100% hold at least a Bachelor's degree, 62% hold at least a Master's degree, and 8% hold a doctorate.
On average, freelancers in Berlin, Germany who have used DAX in their recent projects have 12 years of professional experience, with a single engagement typically lasting around 1.9 years.
The most common languages among freelancers in Berlin, Germany who have used DAX in their recent projects are German (100%), English (100%), and Spanish (14%).
The most common industries among freelancers in Berlin, Germany who have used DAX in their recent projects are Information Technology (93%), Banking and Finance (57%), and Professional Services (43%).
The most common business areas among freelancers in Berlin, Germany who have used DAX in their recent projects are Business Intelligence (93%), Information Technology (93%), and Project Management (64%).
Main locations of FRATCH Experts, who have recently used DAX
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Berlin 
Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg 
Hamburg 
Munich 
Cologne 
Frankfurt 
Dusseldorf Request a free demo
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Philipp Thomaschewski
FRATCH CEO
