
Data Modeling Expert in Berlin
for reliable data structures, matched in minutes with vetted specialistsHire experts who design relational, dimensional and graph-based data models, align schemas with business processes, and prepare clear documentation for analytics or operational systems. FRATCH finds a precise match quickly from vetted, available freelancers.
Meet FRATCH Experts in Berlin, who have recently used Data Modeling
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 using a unified data model as the company-wide single source of truth
- Consolidation of previously isolated reporting logic in a central semantic model – elimination of redundant files, manual data transfers and inconsistent metrics across 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; enabling 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 specific recommendations for action
- AI Solution Architecture: Connecting Business Central and Power BI to LangDock via MCP (Model Context Protocol) for AI-supported data usage
- Development 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 delivery 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
Vili D.
Last position:
Technical Lead, Data Engineer at Mercedes-Benz Consulting
Optimized the data architecture (Medallion) to better decouple processing steps and improve transparency and reproducibility
Ensured the technical quality of data processing in Databricks by introducing schema enforcement, data quality checks, and a structured data architecture
Pipeline orchestration with Azure Data Factory
Improved and automated the development and deployment process by integrating Git and GitHub Actions
Technical leadership of the Data Engineering team (3 employees)
Led workshops to optimize and stabilize the data platform and development process
Collected and prioritized new requests and 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
Fred K.
Last position:
Project Manager Digital Restructuring at Northrop Grumman LITEF GmbH
Assignment focus: Leading digital restructuring in a highly regulated Defence and Aerospace environment. Focus areas: improving data flows and data quality, introducing paperless data collection in production, integrating machine data for automatic SPC calculation and process control, as well as cybersecurity analyses.
Main objectives:
- Analyze, structure and improve data flows and data quality in production
- Introduce paperless data collection in manufacturing areas
- Integrate machine data for automatic SPC data calculation and process control
- Implement digital restructuring while meeting regulatory requirements in the Defence environment
Measures and results:
- Analyzed and structured existing production data flows as a basis for control and reporting
- Established mechanisms to ensure data quality and availability across manufacturing areas
- Introduced paperless data collection in production — replacing manual and paper-based processes
- Integrated machine data for automatic SPC data calculation (control charts, Cp/Cpk) and real-time process control
- Conducted cybersecurity analyses in the production and IT environments
Area of responsibility: Project management for digital restructuring, paperless production, data management and data quality in a highly regulated Defence and Aerospace environment.
Mansharan K.
Last position:
MS Fabric | Power BI Consultant at Gropyus Technologies GmbH
- Architected an enterprise Fabric-based data-product governance framework, authoring data contracts and schema-validation KPIs to enforce completeness, freshness and SLA standards across reporting data products.
- Operationalized pipeline observability by integrating Dagster orchestration data into automated data-health reporting, proactively validating data integrity and pipeline reliability before production reporting layers refresh.
- Established BI-as-Code and end-to-end governance through Git-based version control, CI/CD and Alpha→GA release management, strengthening lineage, schema-drift detection and production change traceability.
Alexander Z.
Last position:
Senior Data Solutions Engineer at VMware Inc.
- Architected and deployed private cloud data platform on VMware vSphere, integrating Greenplum MPP, Apache Kafka, Kubernetes, and Apache Solr, and developed real-time ingestion pipelines with Kafka Connect and Schema Registry.
- Led Oracle Exadata to Greenplum migration, rearchitected data models, optimized storage, implemented RabbitMQ with Debezium for CDC, and deployed VectorDB for Generative AI.
- Designed and executed multi-cloud migration PoC across AWS, Azure, and GCP, defined KPIs for throughput, latency, and cost efficiency, executed bulk data transfers, validated analytics and streaming workloads, and delivered full-scale architecture recommendations.
- Assessed legacy on-premises infrastructure and designed modern cloud-native data platforms using Greenplum and containerized microservices, advising on scalability, disaster recovery, and high-availability.
Michael B.
Last position:
Product Analytics Consultant - Trust & Safety at Kleinanzeigen
- Detecting fraud patterns and implementing effective anti-fraud rules while maintaining acceptable false positive rates to ensure a balance between safety and a frictionless user experience
- Supporting ideation and roll-out of new trust and safety features to the platform to block fraudulent activity and increase user awareness for fraud
- Supporting Product, Development and Customer Support with BI reports and further guidance to identify and fight fraud and policy violations
Nitin B.
Last position:
Financial Analytics Lead at Independent Consultant
Led FP&A tech transformation for a 9-figure business – from resolving legacy technical debt to leading AI-native EPM implementation
- Driving end-to-end FP&A transformation, from architecture redesign through EPM tool selection to rollout
- Ran evaluation of 12+ EPM platforms, from vendor negotiation to selection framework tied to long-term planning
- Diagnosed constraints in financial planning architecture, presented findings to the CFO, and secured executive mandate to redesign FP&A infrastructure from the ground up
Darav T.
Last position:
COO & Co-Founder at MedTech Startup
Established and streamlined business development and sales processes, including implementation of CRM systems, marketing strategies, and search engine advertising
Expanded market reach through targeted outreach efforts
Implemented effective B2B marketing strategies to enhance brand visibility and product reach
Developed a strong sales pipeline focused on long-term relationship building and customer retention
Played a key role in the product development lifecycle from ideation to execution, ensuring compliance with market demands and regulatory standards
Led medical and technical teams to drive product development and company growth
Bidya B.
Last position:
Product Manager – Payments & Platform at Pipedrive
CRM and revenue platform managing billing and subscription workflows.
- Scaled payments infrastructure across data products, direct debit expansion and automated abuse prevention, generating $416K in annualized operational savings ($8K/week) by eliminating redundant gateway calls.
- Owned backlog and sprint execution for autonomous checkout abuse detection pipelines, designing real-time risk guardrails and velocity heuristics that blocked card testing attacks.
- Architected enterprise billing migrator user stories and data reconciliation mechanisms, achieving zero-downtime subscription state transitions and cutting $60K in infrastructure overhead.
- Expanded European direct debit (SEPA) payment capabilities, managing cross-squad API dependencies and automated webhook error-handling to eliminate checkout friction.
Mladen B.
Last position:
Senior SAP BW/4HANA / BPC / SAC Consultant – Freelancer at Company in Berlin and Belgium in the utilities sector
- Implementation of data models in BW/4HANA, ABAP CDS Views, HANA Calculation Views, AMDP Scripts, OData Services, object-oriented ABAP in BW, SAC.
- Adjustments in BPC for BW/4HANA.
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.
Özlem A.
Last position:
Adjunct Lecturer at Whitecliffe University of Applied Sciences
- Teaching Introduction to Data Science Course in MBA Programme
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.
Gyan P.
Last position:
Senior DevOps & Cloud Architect, Head of Infrastructure at Bosch
- Azure Landing Zone architecture: Design and implement a hub-spoke, multi-region-capable Azure landing zone that provides the secure, scalable and highly available foundation for autonomous driving and parking data/ML platforms.
- Compliance through Azure Policy: Enforce security and compliance standards across Azure subscriptions via Azure Policy (guardrails, deny/audit/deploy-if-not-exists), consistently achieving top-tier infrastructure security scores.
- Infrastructure-as-Code: Automate provisioning and configuration of Azure resources with Terraform and Bicep, with changes delivered through reviewed CI/CD pipelines.
- Azure Databricks and Unity Catalog: Extensive experience operating Azure Databricks with Unity Catalog for centralized data governance, access control, lineage and auditing across workspaces.
- Monitoring and observability: Built and run infrastructure monitoring and logging with Azure Monitor, Prometheus, Grafana and Loki, covering platform health, alerting, troubleshooting and capacity trends.
- Platform at scale: Architected and operate cloud data/ML platforms supporting multi-PB vehicle data ingestion and simulation; orchestrate Argo Workflow-based recompute and autolabeling services scaling to 100k+ concurrent pods on AKS.
- Reproducible ML: Established reproducible training environments with dataset versioning and provenance tracking, enabling auditing and experiment reliability.
- Operational excellence: Automated GitHub organization management and CI/CD pipelines, improving developer productivity and deployment reliability; automated recurring operational processes.
- FinOps: Developed transparent resource utilization and cost-tracking for multi-project Azure environments.
- Leadership: Technical lead for infrastructure topics, collaborating with development, DevOps and security teams; technical interviewer and recruitment panel member for scaling the engineering team.
Discover over 15,000 top freelancers
Statistics of experts using Data Modeling
Aggregated from the professional profiles of matched freelancers.
Experience
15 years (Germany: 18 years)

Position duration
1.9 years (Germany: 3 years)

Positions per freelancer
10 (Germany: 12)

Top business areas
Information Technology, Business Intelligence, Product Development

Top industries
Information Technology, Banking and Finance, Professional Services

Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
98% (Germany: 92%)
Master's degree or higher
63%
Doctorate
9%

Certifications per freelancer
2 (Germany: 3)

Most common languages
German, English, Spanish

Speak two or more languages
94% (Germany: 97%)
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Berlin are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging within that range.
Average rates of experts in Berlin using Data Modeling
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 29 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Data Modeling 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 (88%)
- Banking and Finance (47%)
- Professional Services (42%)
- Automotive (38%)
- Healthcare (33%)
- Retail (33%)
- Education (27%)
- Manufacturing (27%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Core purpose
Data modeling defines how information is structured, related and governed inside a system. It turns business concepts into entities, attributes, keys and rules that databases and applications can use consistently. The work may cover conceptual, logical and physical models, from an early domain view to an implementation-ready schema.
Models and methods
Relational modeling uses normalized tables, constraints and relationships for dependable transactional systems. Dimensional modeling organizes facts and dimensions for reporting, while graph modeling represents connected entities and their links. Entity-relationship modeling and UML diagrams help teams review structure before implementation.
Ecosystem and tooling
Strong practice connects modeling decisions with the database, analytics and delivery tools around them.
- Map domains, entities, relationships and data ownership
- Define schemas, keys, constraints and naming conventions
- Support SQL databases, warehouses, lakehouses or graph stores
- Maintain diagrams and documentation in collaborative tools
Common work includes SQL, dbt, BI semantic layers, data catalogs, migration tooling and version-controlled schema changes. The right approach depends on query patterns, governance needs and the systems already used by the team.
When expertise matters
Companies bring in freelance expertise when a new platform needs a stable data foundation, an existing schema has become difficult to change, or reporting teams cannot agree on definitions. Specialists are also useful during mergers, application modernization, warehouse migrations and master data initiatives. In Berlin, remote collaboration often works well, while workshops may still benefit from on-site sessions and clear English or German documentation.
Typical deliverables
A project can produce a conceptual model, logical model, physical schema, entity-relationship diagrams and a data dictionary. It may also include source-to-target mappings, dimensional warehouse models, dbt documentation, migration plans and validation rules.
- Translate business processes into consistent data structures
- Identify duplication, ambiguity and risky dependencies
- Design for performance without losing clarity
- Document ownership, lineage and change impacts
What distinguishes strong experts
The best professionals connect database detail with real operational and analytical needs. They ask how data is created, changed, queried and governed, then explain trade-offs in language that product, analytics and technical teams can use. Look for evidence of careful assumptions, tested models, practical documentation and experience with the specific storage and reporting environment involved.
Frequently asked questions
Before you brief your next project: the most common questions about Data Modeling.
Data Modeling is used to define how information is organized, connected and governed in databases, warehouses and applications. It helps teams create consistent schemas, reduce ambiguity and support reliable transactions, analytics and integrations.
Data Modeling describes information structures at conceptual, logical and physical levels, including business meaning and relationships. Database design focuses more directly on implementing those decisions in a particular database, with indexes, constraints and storage choices.
Data Modeling should follow the system’s access patterns and business goals. Relational models suit transactional consistency, dimensional models support analytical queries, and graph models are useful when relationships and traversal are central to the product.
A strong Data Modeling specialist often brings SQL, database administration awareness, data warehousing and ETL or ELT knowledge. Experience with dbt, data catalogs, lineage, BI tools, cloud storage and migration planning can also be important.
The required depth depends on the scope and risk of the system. A focused schema review may need targeted expertise, while a warehouse redesign, master data program or cross-system migration calls for someone comfortable with complex domains and stakeholder decisions.
Data Modeling is well suited to remote work because diagrams, schemas and documentation can be reviewed collaboratively. Teams in Berlin may still choose on-site workshops for domain discovery, while remote delivery works when communication, access and language expectations are agreed early.
Ask the expert to explain modeling choices using your actual business processes and query needs. Good work shows clear definitions, sensible normalization or dimensional structure, documented assumptions, traceable changes and practical validation rather than diagrams alone.
A Data Modeling freelancer should clarify the source systems, target technologies, data owners, quality problems, delivery stages and decision-makers. They should also confirm whether the outcome is a new schema, a migration, reporting models, governance documentation or a combination.
The average hourly rate of freelancers in Berlin, Germany who have used Data Modeling in their recent projects is 92 €, which corresponds to a daily rate of about 735 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used Data Modeling in their recent projects, 98% hold at least a Bachelor's degree, 63% hold at least a Master's degree, and 9% hold a doctorate.
On average, freelancers in Berlin, Germany who have used Data Modeling in their recent projects have 15 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 Data Modeling in their recent projects are German (98%), English (94%), and Spanish (15%).
The most common industries among freelancers in Berlin, Germany who have used Data Modeling in their recent projects are Information Technology (88%), Banking and Finance (47%), and Professional Services (42%).
The most common business areas among freelancers in Berlin, Germany who have used Data Modeling in their recent projects are Information Technology (94%), Business Intelligence (80%), and Product Development (76%).
Main locations of FRATCH Experts, who have recently used Data Modeling
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.
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