
Data Warehouse Expert
for trusted analytics, matched in minutes from over 15,000 CVsHire experts who design analytical data models, build reliable ETL and ELT pipelines, and deliver cloud data warehouse solutions across Snowflake, BigQuery, Redshift and Microsoft Fabric. FRATCH connects you with vetted, available freelancers through fast, precise AI matching.
Meet FRATCH Experts who have recently used Data Warehouse
Jürgen K.
Last position:
Interim Management / Company Succession – Process Analysis & Stakeholder Management
As part of preparing and providing operational support for a company succession:
- Company analysis: Analysis of the overall economic, organizational and structural situation (current status, strengths/weaknesses, potential risks)
- Process analysis: Recording and documentation of all relevant business processes (BPMN, UML) in the areas of management, finance, purchasing, sales and operations
- Process optimization: Identification of weaknesses and inefficiencies, development and operational implementation of improvement measures
- Stakeholder management: Identification and analysis of all relevant internal and external stakeholders; structured communication and coordination with former and future owners, shareholders and employees
- Change management: Supporting employees and management throughout the handover process; building acceptance and trust among all parties involved
- Governance & documentation: Preparation and handover of structured operating manuals, process documentation and an organizational manual for the new business owner
- Risk & weakness analysis: Assessment of operational and strategic risks in the handover process and development of recommendations for action
- Operational support: Interim assumption of management responsibilities; ensuring business continuity during the transition phase
Fadi S.
Last position:
Data Scientist at E-Commerce
- Designed and developed a production-ready data platform based on Microsoft Fabric for an e-commerce company
- Built a scalable Medallion Architecture (Bronze, Silver, Gold) with Lakehouse and Delta Lake as the foundation for analytics and AI applications
- Developed a metadata-driven ETL platform with PySpark and a generic Incremental Loading Framework (SQL Server → Bronze → Silver → Gold)
- Developed reusable components for Microsoft Fabric Notebooks to standardize data integration, transformation, and validation
- Implemented automated data pipelines including monitoring, logging, error handling, and Delta MERGE strategies for stable production operations
- Built a business layer architecture for reporting, data warehousing, and reusable data products
- Integrated heterogeneous data sources via SQL Server, REST APIs, Amazon SP API, and Google Analytics 4
- Worked closely with business departments to analyze business requirements and translate them into scalable data architectures
Technologies: Microsoft Fabric, PySpark, Python, SQL Server, Delta Lake, Lakehouse, OneLake, Data Factory, Data Warehouse, REST APIs, Amazon SP-API, Google Analytics 4
Vladimir E.
Last position:
Technical Project Manager / Freelancer at Deutsche Bank
- Engaged within the Information and Security Management division for a Proof of Concept (PoC) project focused on developing a Client Security Portal.
- Managed project resources across the UK, India, and Germany.
- Architected and deployed the PoC solution leveraging a modern Java stack, Google Cloud Platform (GCP) with Kubernetes, and Active Directory (Entra ID) for authentication.
- Implemented OAuth 2.0 for authorization.
- Ensured DORA alignment according to Deutsche Bank security rules and regulatory preparation.
Michael S.
Last position:
Data Analyst, Data Scientist at CompuGroup Medical Deutschland AG, docmetric GmbH
Development of AI and traditional models to analyze medical and patient data, including medication analyses, diagnosis analyses, forecasts, procedure analyses, dosage analyses, comorbidity analyses, prescription analyses, patient potential analyses, referral profile analyses, and prescription analyses. Analyses in the field of Real World Evidence.
Healthcare IT, pharmaceutical industry
Tasks, responsibilities:
- Gathering customer requirements
- Planning the subproject
- Designing and defining KPIs
- Designing and developing models and visualizations of results using customer dashboards
- Developing and implementing data warehouse adaptations
- Deriving recommendations for action
Methods, technologies:
- Simulation
- Artificial intelligence
- Python
- R
- SQL
- Microsoft Power BI
- Amazon Web Services
- Elasticsearch
- PostgreSQL
- Databricks
- Multivariate statistics
Geoffrey G.
Last position:
Lead Researcher at York St John Business School
- Independently designed, conducted and evaluated a research project examining dynamic management capabilities (DMCs) in the implementation of HR practices at different management levels (top, middle and frontline management).
- Focus on the strategic role of frontline management in implementing effective HRM measures.
- Self-initiated project with academic collaboration involving York St John University, Robert Kennedy College and the University of Cambridge.
Christian P.
Last position:
Power BI Developer & Consultant in an Agile Team (SAFe) at DB InfraGO AG
Further Development of the Integrated Capacity Management Platform InKa
Rail Infrastructure
- Power BI shared datasets and reports via DirectQuery on Amazon Redshift (timetable, construction and capacity data)
- Automated report testing with Playwright in Docker containers, version control with GitLab
- Concept and implementation of user communication (information page, changelog, known bugs)
Initial Situation:
The integrated capacity management platform (InKa) is a monitoring and analysis platform for data from timetable and capacity management: customer orders, infrastructure, timetable operations, construction, as well as route capacity and width. The data is stored and linked in a structured data warehouse. This makes it possible to create complex KPIs across different source systems and display current and future route utilization as well as construction activity across the network. The goal is to improve strategic, medium-term and intra-year planning and provide users with easy access to capacity management analyses.
Measures:
- Enablement: Introduction of Power BI with training and workshops for development teams.
- Consulting & Design: Problem analysis, concepts and solution consulting for reporting requirements; implementation of reporting solution plans.
- Data Model: Development of shared datasets with DirectQuery on Amazon Redshift and import tables; DAX measures for utilization and capacity KPIs.
- Development: Data analysis, data transformation and visualization in Power BI; extension and migration of business applications to Power BI; AI-supported development in VS Code with GitHub Copilot.
- Test Automation: Automated testing of Power BI reports with Playwright; setup of the test environment in Docker containers.
- User Communication: Concept and implementation of a central information page with hot news, changelog and known bugs, access from reports with report filters, updates via Power Automate.
- Deployment: Management of Power BI Deployment Pipelines across the DEV, TEST and PROD environments.
- Agile Working: Work in an agile team according to SAFe (Scaled Agile Framework); refinement of user stories and features including effort estimates in Jira; version control with Git and GitLab.
- Quality & Documentation: Reviews for quality assurance, error analysis and resolution, technical documentation in AsciiDoc and Confluence.
Technologies: Power BI, DAX, Power Query, DirectQuery, Amazon Redshift, Git / GitLab, Deployment Pipelines, Playwright, Docker, AsciiDoc, Power Automate, Jira, Confluence, VS Code with GitHub Copilot, SAFe
Results:
- Capacity management KPIs from multiple source systems combined on one platform
- Transparency regarding current and future route utilization and construction activity across the network
- Easy access to analyses for users
- Automated regression testing of Power BI reports
- User communication implemented: central information page with hot news, changelog and known bugs, directly accessible from the reports
Bruno P.
Last position:
Product Owner
Design and implementation of a “unified-commerce” platform for SMEs to sell and deliver multi-product bundles
Achievements: Designed and delivered a fully functional, CPQ-based “buy flow” process for configuring and selling multi-product bundles on time, and secured customer approval within the specified 90-day timeframe. Successfully tested the integration of various interface services for customer search, customer data enrichment, address validation, service qualification, phone number validation, credit checks, appointment selection and quote-to-order transition.
Responsibilities:
- Strategic goal delivery: Translating strategic customer goals into action and delivering them (e.g. release content, business value).
- Backlog management: Creating and refining backlog items (Initiatives, Epics, User Stories, Defects) in close coordination with the project team and client.
- Prioritization & releases: Owning Product Backlog prioritization and delivery of defined release goals.
- Deliverable tracking: Tracking work completed by suppliers and the client.
- Roadmap & release planning: Developing and delivering roadmaps and release plans together with the client.
- Scope ownership: Owning the contractually agreed scope of work.
- Claim management: Proactive claim and change management with the client.
- Team coordination: Managing and coordinating the development team.
- Leveraging synergies: Using synergies between client projects and product development.
- Proposal preparation: Preparing proposals (with supervision) and presenting them in person to clients and partners.
Skills: Developing, communicating and delivering product visions; Product Backlog Management; Stakeholder Management; Regular reporting to management and steering committees; Requirements analysis & engineering; Planning and documenting workshops; Functional leadership and coordination of (distributed) project teams and external service providers; Epic Management; User Story specifications; Creating use cases, supporting software testing and User Acceptance Testing (UAT); Release Management and sprint planning; TO-BE process design; Process optimization; Planning, designing and specifying interfaces to existing and new systems; Identifying, assessing and managing project risks; Proactive claim and change management; Facilitating sprint planning and reviews; Data migration; Scrum; Kanban; REST API; JSON; XML; BPMN; UML; Jira; Confluence
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.
Umut G.
Last position:
Data Architect at BA Technology
I am an experienced data engineer specializing in end‑to‑end data integration, cloud DWH architectures, and high‑quality, governed data products.
I delivered following projects and engagements as a freelancer.
- Data Migration of CRM System for AL-FA Objekt Service Gmbh
- Microsoft Software Resales Partnership
I am looking for freelance roles like: Freelance Data Engineer Cloud Data Warehouse Architect Data Modeling & Architecture Consultant MDM & Data Governance Specialist BI & Analytics Developer
Technical Focus Areas
- Data Engineering & Integration: SQL Server/SSIS, Informatica PowerCenter/IDQ, Talend, Kafka, Azure Data Factory – Delta/CDC/ELT patterns, robust pipelines, monitoring/recovery, data lineage & impact analysis, medallion architecture Bronze/Silver/Gold layers
- DWH & Cloud: Azure SQL / Data Lake / Synapse, AWS Redshift/S3, on‑prem SQL/Oracle – scalable data marts with a strong cost/benefit focus.
- Data Modeling: Atomic (Inmon) and Dimensional (Kimball), Data Vault (Linstedt), Domain‑Driven Design, clear lineage & contracts.
- MDM & Governance: Informatica MDM, IBM MDM, stewardship processes, data quality rules, survivorship/XREF, catalog/glossary, SIF/BES/REST publication.
- Analytics/BI: Power BI, SSAS, Cognos – business‑ready, maintainable data products.
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.
Martin R.
Last position:
Fractional CIO - IT Carve-out & Post-Merger Integration at XTRONIC GmbH & iinovis GmbH (Accursia Capital)
Carve-out and post-merger integration of two automotive engineering companies under Accursia Capital, 6 locations in Germany and Spain, 250+ employees after the carve-out. Reporting directly to the investor, with overall IT responsibility for both companies.
- Consolidation of the ERP landscape into one instance (replacement of SAP legacy)
- TISAX certification
- M365, identity and data center migration
- Replacement of end-of-life infrastructure
- Weekly investor reporting
Results:
- Critical infrastructure risk eliminated, backup and platform stabilized
- One IT organization established for both companies
- Weekly investor reporting successfully established
Robert K.
Last position:
Interim Manager | Group Manager in the IT Operations & Digitalization division „Databases, Operations & Support" (DOS) at Landwirtschaftliche RentenBank
- Functional management and further development of a team of 28 IT employees (internal & external) in the areas of HelpDesk / HelpLine, RHEL (Red Hat), MUREX (trading system & applications), SAP basic operations
- Management of external service providers (including FI-TS for SAP basic operations)
- Personnel, resource and budget planning for the IT department and projects
- Introduction and establishment of regular communication formats (regular meetings, team and cross-departmental meetings)
- Ensuring application operations in a hybrid environment (Windows / Linux with database and web/application servers)
- Support for transformation projects to modernize the application landscape (e.g. DevOps approaches)
- Building and introducing digital IT procurement for hardware & software (e.g. with DELL)
- Development and management of the „IT Operations DOS" department based on the company strategy
- Preparation of management reports & decision papers on IT projects, optimization potential and automation opportunities
- Representing the department in Rentenbank committees and ensuring regulatory compliance (BaFin §44, DSGVO, BSI, MaRisk)
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.
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.
Eric B.
Last position:
Quality Assurance Manager (QSV) at Federal Employment Agency
Supporting the International Web Presence of the BA (IntWeb) project in the area of quality management, with the aim of taking responsibility for the quality of processes and project results while considering BA standards. The main objectives of the project are to give professionals abroad a quick overview of their opportunities for moving to Germany and to enable them to take the necessary steps consistently and with digital support by providing access to services.
Defining the general guidelines in the QA handbook
Summarising test results in QA reports for PLA
Analysing project results for improvement opportunities
Quality management of requirements analysis (particularly processes, methods, and tools)
Ensuring compliance with SERA requirements
Creating a cross-project test concept
Agreeing on sprint completion reports
Formal review of work results in accordance with requirements and/or the project plan
Contact person for internal audit, external audits by auditors, or BRH (Federal Court of Audit)
Technologies: JIRA, Confluence, MS Office, GitLab, Kubernetes
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
90%
Master's degree or higher
59%
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 5 Nov 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 5 Nov 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 (85%)
- Banking and Finance (58%)
- Professional Services (49%)
- Manufacturing (37%)
- Automotive (35%)
- Insurance (35%)
- Retail (34%)
- Healthcare (33%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Foundations
A data warehouse centralizes structured information from operational systems so companies can analyze performance, customers, finance and operations from a consistent source. It separates analytical workloads from transactional databases and supports governed reporting, dashboards and business intelligence.
Data Pipelines
Specialists connect applications, databases, APIs and files through ETL or ELT pipelines. They clean, validate and transform data before loading it into an enterprise data warehouse or cloud data warehouse. Strong delivery includes scheduling, retries, lineage, monitoring and clear ownership of failed loads.
Models And Tools
Modern warehouse work spans dimensional modeling, star schemas, data marts and semantic layers. Common ecosystems include Snowflake, Google BigQuery, Amazon Redshift, Microsoft Fabric, Azure Synapse Analytics and Databricks SQL. Professionals may also use dbt, Airflow, Fivetran, Matillion, SQL and Python.
Where It Helps
Companies bring in freelance expertise when reporting is slow, source systems disagree or a migration needs careful control.
- Consolidating finance, sales and customer data
- Moving from on-premises systems to cloud warehouses
- Creating trusted metrics for BI and executive reporting
- Improving pipeline reliability, cost control and query performance
Project Signals
A company usually needs specialist support when teams maintain conflicting spreadsheets, analysts cannot trace a metric back to its source, or dashboards depend on manual exports. Freelance professionals can assess the current architecture, define a migration path and leave documentation that internal teams can maintain.
Quality Markers
Strong professionals ask how data is produced, used and governed before choosing a warehouse pattern. They test transformations, protect sensitive fields, monitor freshness and explain trade-offs between cost, speed, flexibility and control. Look for practical evidence of reliable models, recoverable pipelines and clear communication with analysts and business stakeholders.
Frequently asked questions
Before you brief your next project: the most common questions about Data Warehouse.
A data warehouse stores prepared data from multiple operational systems for analysis and reporting. Companies use it for dashboards, financial reporting, customer analysis, forecasting and consistent business metrics.
A data warehouse usually stores structured, modeled data that is ready for governed analysis. A data lake can hold raw structured, semi-structured and unstructured information, while a lakehouse combines parts of both approaches.
A strong data warehouse specialist may work with Snowflake, BigQuery, Amazon Redshift, Microsoft Fabric, Azure Synapse Analytics or Databricks SQL. Useful adjacent skills include SQL, Python, dbt, Airflow, cloud security, data quality and business intelligence tools.
The right data warehouse professional depends on the project scope, source complexity and governance requirements. A focused reporting model may need a different profile from a migration involving many systems, sensitive data and demanding service levels.
Yes, much data warehouse work can be delivered remotely through cloud access, version control, documentation and regular planning sessions. On-site collaboration can still help when source systems are difficult to access or business teams need close workshops.
Review how the data warehouse professional handles data modeling, testing, lineage, monitoring and documentation. Ask for examples of resolving inconsistent source data and improving reliability without hiding important trade-offs.
An enterprise data warehouse is useful when multiple departments need shared definitions, governed access and dependable cross-functional reporting. It is less suitable as a default repository for every raw file or exploratory dataset.
Before starting data warehouse work, clarify source systems, data owners, reporting goals, security rules, refresh expectations and acceptance criteria. A clear discovery phase also exposes undocumented transformations and prevents teams from rebuilding inconsistent metrics.
The average hourly rate of freelancers who have used Data Warehouse in their recent projects is 104 €, which corresponds to a daily rate of about 831 € based on an 8-hour working day.
Of the freelancers who have used Data Warehouse in their recent projects, 90% hold at least a Bachelor's degree, 59% 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 (20%).
The most common industries among freelancers who have used Data Warehouse in their recent projects are Information Technology (85%), Banking and Finance (58%), and Professional Services (49%).
The most common business areas among freelancers who have used Data Warehouse in their recent projects are Information Technology (98%), Business Intelligence (84%), and Project Management (73%).
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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