
Data Warehouse Expert
for reliable analytics, matched in minutes with vetted and available freelancersHire experts who design analytical data models, build reliable ETL and ELT pipelines, and optimize cloud warehouse performance. FRATCH connects you with precise matches from vetted, available freelancers who can contribute quickly to your data program.
Meet FRATCH Experts who have recently used Data Warehouse
Amit S.
Last position:
IT Project Manager – SAP S/4 HANA Delivery Consultant at CHG Meridian UK Limited
- Managed the integrated SAP S/4HANA delivery plan for 12 BTS workstreams, including milestone, dependency, and governance management.
- Coordinated end-to-end delivery across Business, IT, and Operations, focusing on execution, readiness, and issue resolution.
- Responsible for RAID, test, cutover, and deployment management to ensure a successful go-live.
- Implemented KPI tracking, status reporting, and PMO governance to provide transparent management of performance, risks, and escalations.
Ebru A.
Last position:
Product Analytics & App Tracking Consultant at EnBW mobility+ AG & Co. KG
- Product Analytics, Mobile App Tracking & Tracking Governance (B2C Mobility App) – agile project management (Scrum/Kanban)
- Product Ownership for Product Analytics and Mobile App Tracking of the EnBW mobility+ app; gathering, prioritizing, and translating business requirements into actionable concepts and Azure DevOps user stories with acceptance criteria.
- Derivation of tracking requirements when introducing new app features (including Resilient Map), definition of tracking parameters (screens, events, custom definitions), and ensuring privacy-compliant tracking (Firebase, GA4, Adjust) based on the tracking concept.
- Design and adaptation of dashboards and funnel reporting for campaigns (GA4 validation, onboarding and order flow analyses, conversion funnels, charging start flow) to identify drop-off points and optimization potential.
- Management of the technical raw data export (Adjust to BigQuery) and connection to the data warehouse/data lakehouse, including data mapping; collaboration with international development teams, Data Engineering, Marketing/Sales, and Product Management.
- Establishment of standardized tracking architecture, naming conventions, and governance; analysis and expansion of tracking (new features and “blind spots”), test design, handover to testers, and quality assurance and approval before releases; documentation in Conceptboard.
Wolfgang O.
Last position:
Project Manager at EnBW - Netze Südwest
- New development and further development of the existing MS Dynamics CRM
IT systems: Microsoft Dynamics Customer Service, SharePoint, DevOps, SAP IS-U
- CRM implementation / further development
- Taking over from the previous service provider
- Business process analysis
- Agile project organization
- Business analysis / requirements engineering with AI support
- Use of AI in development
- Analysis of master data processes
- CRM customer data management
- Requirements documentation
- Stakeholder management
- Workshop moderation
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.
Michael S.
Last position:
Establishment of Compliance/TPRM at Haftpflichtkasse
Establishment of Compliance Department & DORA Operationalization
- Establishment of a complete compliance organization in accordance with DORA
- Development and operationalization of the SfO
- Use of AI agents for automation:
- Evaluation of due diligence questionnaires including risk classification
- AI-supported contract analysis (DORA/MaRisk compliance)
- Monitoring of external data sources (cyber incidents, newsfeeds)
- Establishment of a decentralized risk and action register
- Preparation of GAP analyses and derivation of measures
- Establishment and maintenance of the Outsourcing Information Register
- Use of proprietary TPRM frameworks, checklists and process models
Establishment of Compliance Department & DORA Operationalization
- Establishment of a complete compliance organization in accordance with DORA
- Development and operationalization of the SfO
- Use of AI agents for automation:
- Evaluation of due diligence questionnaires including risk classification
- AI-supported contract analysis (DORA/MaRisk compliance)
- Monitoring of external data sources (cyber incidents, newsfeeds)
- Establishment of a decentralized risk and action register
- Preparation of GAP analyses and derivation of measures
- Establishment and maintenance of the Outsourcing Information Register
- Use of proprietary TPRM frameworks, checklists and process models
- Project controlling - presentation and structured measurement of project goals achieved as part of management reporting.
- Overall responsibility for establishing a Compliance, Governance and Risk organization
- Establishment of an integrated GRC model and executive reporting for the Management Board.
Hans-Dieter G.
Last position:
Training as an AI Expert
I continuously expand my expertise in AI and automation. I work with ChatGPT, OpenAI, Manus, Gemini, MS CoPilot, APIs, LangChain, Hugging Face, Manus, TensorFlow, and Auto-GPT, as well as Python-based ML frameworks and MLOps tools, to intelligently transform traditional software development, analysis, and testing processes.
Bruno P.
Last position:
Product Owner
Design and implementation of a „unified-commerce“ platform for SMEs to sell and deliver multi-product bundles
Achievements: On-time design, delivery and customer acceptance of a 100% functional, CPQ-based „buy flow“ process for configuring and selling multi-product bundles within the specified time frame of 90 days. Successfully tested integration of various interface services for: customer search, enrichment of customer data, address validation, service qualification, phone number validation, credit check, appointment selection and Quote-to-Order transition.
Responsibilities:
- Strategic goal implementation: Derivation and implementation of strategic customer goals (e.g. release content, business value).
- Backlog Management: Creation and elaboration of backlog items (Initiatives, Epics, User Stories, Defects) in close coordination with the project team and customer.
- Prioritization & Releases: Responsibility for prioritizing the Product Backlog and implementing defined release goals.
- Deliverable Tracking: Tracking work results on the supplier and customer side.
- Roadmap & Release planning: Development and implementation of roadmaps and release plans together with the customer.
- Scope responsibility: Responsibility for the contractually agreed scope of services.
- Claim Management: Active claim and change management towards the customer.
- Team coordination: Management and coordination of the development team.
- Use of synergies: Use of synergies between customer projects and product development.
- Proposal preparation: Preparation of proposals (with supervision) and presentation on site to customers and partners.
Skills: Development, communication and implementation of product visions; Product Backlog Management; Stakeholder Management; Regular reporting to management and Steering Committees; Requirements analysis & engineering; Planning and documentation of workshops; Professional leadership and coordination of (distributed) project teams and external service providers; Epic Management; User Story specifications; Creation of use cases, support with software testing and User Acceptance Testing (UAT); Release Management and Sprint Planning; Design of TO-BE processes; Process optimization; Planning, design and specification of interfaces to existing and new systems; Identification, assessment and management of project risks; Active claim and change management; Facilitation of sprint planning and reviews; Data migration; Scrum; Kanban; REST API; JSON; XML; BPMN; UML; Jira; Confluence
Fadi S.
Last position:
Development of a production-ready Enterprise Document AI & Recommendation Platform at Freelancer
- Development of a production-ready Enterprise AI solution for the automated processing of invoices and business documents
- Integration of Azure AI Document Intelligence and LLM technologies into existing business processes
- Development of robust REST APIs for automated document processing and system integration
- Extraction, validation, and storage of structured invoice data in Azure SQL as a base for analytics and machine learning models
- Development of an AI-based recommendation engine with machine learning and deep learning to generate personalized product recommendations based on historical purchase data
- Implementation of logging, monitoring, error handling, and validation mechanisms for stable production use
- Collaboration with business teams to define business rules and integrate the solution into existing enterprise processes
Technologies: Python, Azure AI Document Intelligence, Azure OpenAI, Azure SQL Database, REST APIs, Machine Learning, Deep Learning, OCR, Pandas, JSON, Workflow Automation
Karin A.
Last position:
AI Benchmark Engineer | Native language specialist German at Lilt
- Task Engineering: Evaluating Coding Agents.
- Asset Creation: Building realistic task environments using datasets and files in German. Crucially, these assets must remain in the target language to genuinely measure multilingual handling.
- Prompting & Translation: finding failure points where AI does not work, in German.
- Implementation & Verification: Supporting the development of robust solutions (reference implementations) and write highly reliable, deterministic verifier scripts (using rubric-based judging only when strictly necessary).
- Calibration & Execution: Analyze execution logs and calibrate task difficulty (Easy to Very Hard) using standard Terminal-Bench run configurations against various model tiers (Haiku, Opus).
- Quality Assurance: Participation in a rigorous, 4-layer human quality control process (creation, human review, calibration review, and audit) alongside automated LLM-based checks to ensure fairness, grammatical accuracy, and benchmark integrity.
- Linguistic Review: Reviewing AI benchmark tasks across Hindi, Arabic, Japanese, Chinese, Czech and Turkish.
Songül D.
Last position:
Freelance SAP BW Consultant at DKV Mobility Services
- Designed and implemented enhancements in SAP BW on HANA 7.5 in the context of CRM migration and S/4HANA and BW/4HANA transformation programs
- Migrated SAPI data sources to the ODP framework as part of the S/4HANA migration
- Delivered SAP ECC data to Snowflake using BW data models and Calculation Views for Power BI analytics
- Integrated SAP and non-SAP data sources (including MS Dynamics)
- Improved reporting transparency through consolidated data models
- Optimized data loading processes for FI-CO data sources, significantly improving load times and system performance
Christian F.
Last position:
Department Head (Interim) at Municipal utilities and transport company
- Defined and established the areas of responsibility
- Built a governance model for the department and its areas of responsibility
- IT strategy, project management, process management, and quality and sustainability management
- Developed a communications strategy for the group
- Created the IT strategy
- Designed templates, guidelines, and processes for consistent ways of working
- Recorded strategic guidelines and grouped ongoing projects – derived a roadmap for strategic planning
- Reviewed ongoing projects
- Prepared staffing calculations and capacity planning
- Defined job profiles
Peter H.
Last position:
Consultant Financial Data Warehouse Migration Interfaces Reporting at Large bank / central institution
- New connection of the product interfaces of the Financial Data Warehouse (FDW) directly to the Abacus 360 native interfaces
- IT environment: PC, client-server, Citrix remote client, Oracle DB
- Mapping of FDW outbound to Abacus 360 Native
- Analysis of requirements documents, business concepts, and existing interface rules
- Definition of new FDW rules for outbound to Abacus 360
- Adjustment of test scripts for the new interfaces
- Execution of tests to ensure correct data delivery and processing
Robert K.
Last position:
Interim Manager | Group Leader in the IT Operations & Digitalization Division "Databases, Operations & Support" (DOS) at Landwirtschaftliche RentenBank
- Technical leadership and further development of a team of 28 IT staff (internal & external) in the areas HelpDesk/HelpLine, RHEL (Red Hat), MUREX (trading system & applications), SAP basis operations
- Managing external service providers (including FI-TS for SAP basis operations)
- Personnel, resource and budget planning for the IT department and projects
- Introducing and establishing regular communication formats (weekly status meetings, team and cross-department meetings)
- Ensuring application operations in a hybrid environment (Windows/Linux with database and web/application servers)
- Supporting transformation projects to modernize the application landscape (e.g. DevOps approaches)
- Setting up and implementing a digital IT procurement for hardware & software (e.g. with DELL)
- Developing and managing the "IT Operations DOS" department based on corporate strategy
- Preparing management reports & decision papers on IT projects, optimization potential and automation opportunities
- Representing the department in the advisory boards of the Rentenbank and ensuring regulatory compliance (BaFin §44, GDPR, BSI, MaRisk)
Florian B.
Last position:
Business Architect — Project Organization Blueprint for Restructuring
Tasks & results:
- Developed measures to improve management steering during a restructuring program (approx. 80 participants)
- Set up a PMO to ensure transparency, reporting and data-driven decisions
- Created an integration template to transfer team s...
Daryoosh D.
Last position:
FP&A Data & AI Architect at Epta Group
Scope: Embedded as FP&A Data & AI Architect within the Finance function of a major European refrigeration manufacturer, leading the transformation of manual, fragmented financial reporting into an automated, governance-driven intelligence platform. Driving the shift from Excel-based controlling to structured data architecture, Power BI analytics, and AI-assisted financial operations.
Financial Data Integrity & ERP Governance
- Initiated and led GL vs. subledger reconciliation investigations, identifying and resolving structural mismatches between General Ledger and subledger data that had gone undetected prior to engagement
- Conducted asset analysis to identify items missing from General Ledger postings, surfacing gaps in fixed asset tracking and period-end completeness
- Validated SAP reports, establishing baseline data quality standards for Finance team consumption
- Established systematic SAP data validation framework ensuring ongoing integrity between ERP postings and downstream reporting outputs
Finance Reporting Transformation
- Designed and implemented a structured Transformation Project approach for converting manual Finance reports into fully automated processes
- Created and owns the Data Reporting Audit Log; a centralized tracking system capturing report owners, stakeholders, data sources, manual effort estimates, and automation opportunity scores across the Finance function
- Mapped the full reporting landscape identifying quick-win automation targets and strategic Power BI migration candidates
- Actively reducing manual Excel and PowerPoint dependency across FP&A workflows; replacing point-in-time snapshots with live, governed data models
Power BI & Analytics Enablement
- Introduced and presented Power BI as the strategic reporting platform to Finance leadership, building internal buy-in for the BI transformation roadmap
- Designed initial Power BI architecture aligned with SAP, Salesforce and Oracle data structures and FP&A reporting requirements
- Established report ownership, governance documentation, and data lineage standards enabling sustainable self-service analytics across the Finance team
Transformation Infrastructure & Collaboration
- Configured and deployed Jira as the transformation project management hub, establishing structured sprint workflows, backlog management, and progress visibility for Finance IT initiatives
- Proposed and initiated a dedicated FP&A Communication & Transformation Hub, a structured cross-functional forum aligning Finance, IT, and business stakeholders around the reporting transformation roadmap
- Positioned the Finance function as an active driver of data governance and digital transformation within the broader organization
Outcomes
- GL/subledger reconciliation gaps identified and investigation framework established within first two weeks of engagement
- Data Reporting Audit Log deployed; first structured inventory of Finance reporting landscape in company history
- Power BI transformation roadmap presented and approved by Finance leadership
- Jira-based project governance live; Finance transformation now tracked with full sprint visibility
Technologies: SAP FI/CO · Power BI · DAX · SQL · Excel (advanced) · Power Query (M) · Power Automate · VBA · Jira · Microsoft 365 · SharePoint · Salesforce (Sales Data) · Oracle HCM · Python
Discover over 15,000 top freelancers
Statistics of experts using Data Warehouse
Aggregated from the professional profiles of matched freelancers.
Experience
22 years

Position duration
3.2 years

Positions per freelancer
13

Top business areas
Information Technology, Business Intelligence, Project Management

Top industries
Information Technology, Banking and Finance, Professional Services

Certification focus areas
Information Technology, Project Management, Business Intelligence
Bachelor's degree or higher
88%
Master's degree or higher
56%
Doctorate
10%

Certifications per freelancer
3

Most common languages
German, English, French

Speak two or more languages
95%
Based on our profile pool as of 26 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology 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 using Data Warehouse
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 26 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Data Warehouse 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 (86%)
- Banking and Finance (57%)
- Professional Services (50%)
- Manufacturing (37%)
- Insurance (36%)
- Automotive (35%)
- Retail (33%)
- Healthcare (31%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Purpose and foundations
A data warehouse is a central system for storing structured, historical data for reporting, business intelligence and advanced analysis. It brings together information from operational databases, applications, files and external services without burdening those source systems. Teams use it to create consistent metrics, trusted dashboards and a shared view of business performance.
Modeling approaches
Specialists shape raw data into models that are clear, reusable and efficient for analytical workloads. They choose dimensional, normalized or data vault patterns based on reporting needs, governance requirements and the expected pace of change. Fact tables, dimensions, slowly changing attributes and semantic layers help different teams interpret the same data consistently.
Pipelines and tooling
Data warehousing includes ingestion, transformation, orchestration, testing and monitoring. Common environments include Snowflake, Google BigQuery, Amazon Redshift, Microsoft Fabric and Azure Synapse Analytics, alongside tools such as dbt, Apache Airflow, Fivetran and Matillion. Strong specialists also work with SQL, Python, APIs, data catalogs and version-controlled deployment workflows.
Where it delivers value
Companies bring in freelance expertise when analytics must become more dependable, scalable or accessible across the organization.
- Consolidate finance, sales, marketing and customer data
- Replace fragile spreadsheet reporting with governed datasets
- Create executive dashboards and self-service BI models
- Migrate an on-premises warehouse to a cloud environment
- Prepare clean historical data for forecasting and machine learning
When outside expertise helps
Freelance specialists are useful during warehouse migrations, platform selection, reporting redesigns and pipeline modernization. They can assess an existing environment, define a target architecture, resolve inconsistent definitions and establish delivery practices without waiting for a permanent team to grow. For distributed teams, remote collaboration works well when ownership, documentation, access and review processes are explicit; local language skills may matter when requirements come from regional business teams.
What strong specialists bring
Quality work combines SQL depth with sound data architecture and a practical understanding of business processes. Look for professionals who can explain trade-offs, trace a dashboard metric back to its source, and demonstrate testing, observability, access control and recovery planning. Strong specialists leave behind documented models, maintainable transformations and clear handover materials rather than a warehouse only they can operate.
Frequently asked questions
Before you brief your next project: the most common questions about Data Warehouse.
A data warehouse stores integrated, historical information for reporting, business intelligence and analysis. It separates analytical workloads from transactional systems and gives teams consistent data for dashboards, planning and performance tracking.
A data warehouse usually stores curated, structured data optimized for reliable queries and governed reporting. A data lake can retain raw structured, semi-structured or unstructured data, so many organizations use both in a broader data architecture.
A strong data warehouse specialist typically works with SQL, data modeling, ETL or ELT, orchestration and cloud infrastructure. Experience with dbt, Apache Airflow, BI tools, data quality testing, security and metadata management is also valuable.
The right level depends on the scope, source systems and consequences of incorrect reporting. A focused pipeline or model change may suit a specialist familiar with the relevant stack, while a migration or enterprise data warehouse redesign calls for someone who has led architecture, governance and delivery across several teams.
Yes, data warehouse work is often suitable for remote collaboration because the core activities use shared repositories, cloud environments and documented workflows. On-site sessions can still help with discovery, stakeholder alignment and workshops, particularly when business definitions are unclear.
Popular data warehouse platforms include Snowflake, Google BigQuery, Amazon Redshift, Microsoft Fabric and Azure Synapse Analytics. The best choice depends on data volume, query patterns, cloud strategy, governance, existing skills and integration requirements rather than brand familiarity alone.
Ask a data warehouse specialist to explain a model or pipeline they delivered, including source quality, testing, performance and failure handling. Review whether they can connect technical decisions to business definitions and show clear documentation, monitoring and ownership practices.
An enterprise data warehouse can be appropriate when several departments need governed, shared metrics across operational systems. It is less suitable as the only solution when teams mainly need inexpensive raw-data storage, rapid exploration or specialized workloads better served by a lake or lakehouse.
The average hourly rate of freelancers who have used Data Warehouse in their recent projects is 105 €, which corresponds to a daily rate of about 836 € based on an 8-hour working day.
Of the freelancers who have used Data Warehouse in their recent projects, 88% hold at least a Bachelor's degree, 56% hold at least a Master's degree, and 10% hold a doctorate.
On average, freelancers who have used Data Warehouse in their recent projects have 22 years of professional experience, with a single engagement typically lasting around 3.2 years.
The most common languages among freelancers who have used Data Warehouse in their recent projects are German (99%), English (95%), and French (21%).
The most common industries among freelancers who have used Data Warehouse in their recent projects are Information Technology (86%), Banking and Finance (57%), and Professional Services (50%).
The most common business areas among freelancers who have used Data Warehouse in their recent projects are Information Technology (98%), Business Intelligence (85%), and Project Management (74%).
Main locations of FRATCH Experts, who have recently used Data Warehouse
Our freelancers and interim experts are at home all over Germany — available on-site in Berlin, Hamburg, Munich and every major business hub, or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.
Countries:
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