Data Warehouse Experts in Vienna
, matched in minutes from over 15,000 CVs with the power of AIHire experts who design reliable analytical data platforms, model business-ready datasets and connect cloud services such as Snowflake, BigQuery or Amazon Redshift. FRATCH matches you quickly and precisely with vetted, available freelancers for your data warehouse project.
Meet FRATCH Experts in Vienna, who have recently used Data Warehouse
Alexander P.
Last position:
Owner & Lecturer at Artellico / Own company for AI governance and data products
- Consulting and interim management at the intersection of IT operations and regulation
- Impact assessment and implementation planning for NISG 2026 and the EU AI Act, including risk management and reporting and evidence processes
- Training for governing bodies and employees on regulatory obligations
- Teaching assignments in Data & Information Management and Human-Machine Interaction at Hochschule Burgenland, since 2023
- Supervision of Master's theses and participation on examination boards
- Industry talks for businesses and educational institutions
- Design and development of data and AI products, platforms and pipelines
- Privacy-first architectures and Zero-Knowledge encryption, cloud-native on EU infrastructure
- Integration with bank account systems using SEPA direct debit mandates and message formats under ISO 20022 (pain.008, camt.053)
- MLOps and AIOps in live operations
- Own applications under my own brand: shared codebase, separate deployment for each target device
- AI-assisted software development (vibe-coding), complete agentic pipelines, code generation, implementation, automated testing, CI/CD and release cycles
- Professional publications on the EU AI Act, NIS2, DORA, CRA and CER as an integrated governance system
- Professional publications on data sovereignty, cloud economics and industrial image processing
- AI governance / compliance: data quality, Responsible AI, EU AI Act readiness, risk classification, AI ethics
Thomas P.
Last position:
Change Manager / AI-driven Finance Transformation at Agrana
Organizational Change Management for the strategic setup of a hybrid GBS rollout as part of a global, AI-driven Finance Transformation. End-to-end management of the OCM architecture in a highly complex on-, near-, and offshore partner setup [IN, PL, RO]. Successful completion of Phase 1 (Initial Strategy) and strategic pausing by the Executive Board after the early disclosure of critical vendor-side OPEX risks
Systemic Baseline Assessment & Stakeholder Diagnostics: Evaluation of the global organization and analysis of the process landscape across 21 countries [AT, DE, CH, FR, UK, SI, RO, UA, RU, US, CD, CN, AR, MX, ZA, IN, JP] to precisely determine change requirements
Architecting the OCM Strategy & Master Roadmap: Independent development of a comprehensive, AI-centered Change Roadmap for end-to-end management of all change initiatives across every project phase
Data Quality & AI Agent Risk Mitigation: Auditing of Process Data Repositories (Celonis analysis); prevention of the ingestion of 58% synthetic training data to safeguard AI model reliability amid geopolitical supply chain tensions
GBS Vendor BAFO Triage & Contractual Commitment: Critical review of Tier-1 vendor pitches and contractual commitment to a reduced Non-STP rate (from 7% to 4%) to avoid extreme manual rework with a planned FTE reduction (-63%)
Executive Enablement & Board Alignment: Design of the GBS Transformation Executive Board Deck to strategically enable C-level executives and transparently present completely overlooked AI tokenization costs
Targeted Change Communications & Engagement: Design and development of audience-specific communication measures to align the highly diverse, globally distributed stakeholder network
Adoption Velocity & Resistance Measurement: Engineering of quantitative measurement tools (Pulse Check Survey) using Behavioral Dyads to identify resistance based on data and ensure sustainable success measurement
Results:
- Avoidance of disruptive misallocations: Stopping the ingestion of 58% synthetic AI-agent training data after discovering only 42% compatibility with existing processes, preventing critical GBS cascade errors
- Significant OPEX & effort reduction: Reduction of the Non-STP rate in Tier-1 vendor BAFOs from 7% to 4% alongside an impending 63% Finance-FTE reduction; avoidance of thousands of time-consuming manual correction processes
- 100% Delivery of the OCM architecture & ROI protection: Delivery of all OCM artifacts (Change Roadmap, Comms Plan, Executive Board Deck, Pulse Check Survey) and protection of ROI by identifying incalculable LLM runtime token costs
Garrett T.
Last position:
Transformation Advisor for SAP at Garrett Tedeman, CPA LLC
Independent practice serving clients in SAC-P and AI-enabled FP&A transformation, including financial model development for PE firm engagements, data cleansing with PowerQuery, workflow automation Claude Cowork, SAC-P scoping task-based assignments (Micro1.ai & Mercor.ai), and SAP partner network development.
- 2026 – Completed significant upgrade training for Positioning Business AI and SAP Cloud ALM Workshop (In-Person). Live Demos • SAC-P & Excel-Based Reports • Claude Partner • SAP Partner#: 1961209 (OE – Build)
- Fall 2026 – New effort to re-train/update past FI/CO, SAP BPC, SAP Platform to S/4 Finance & Core G/L Accounting
- 2026 – Upskilling for Transformation Toolchain. In-Person Workshops: Cloud ALM & SAP BTP • Joule Demo(s)
- 2025 – FP&A processes • Datasphere & Business Data Cloud • Team Workshops for AI • Toolchain & SAP Cloud ALM
- 2024 – Built key credentials on Deloitte S4 Experience • Group Reporting and SAC-P
Octavian G.
Last position:
Oracle DWH Analyst at Infomotion Gmbh
Oracle PL/SQL, Oracle SQL, ITIL
Yunus S.
Last position:
Senior Expert / Program Manager at Dolpheen IT
Senior Expert / Program Manager
IT Services
- Management of internal projects for infrastructure modernization: setup and maintenance of test environments that realistically replicate complex customer networks.
- Resource coordination: planning server capacities and cloud resources for automated test runs.
- Implementation of CI/CD pipelines to integrate automated tests into the build process.
- Evaluation of performance bottlenecks through load testing (NeoLoad/JMeter) to ensure scalability.
Tools / Technologies: Jenkins, Docker, GIT, ButBucket, NeoLoad, Azure
Stefan D.
Last position:
BI Consultant in Controlling at Reutter GmbH
- Extraction, transformation, and cleansing of data from Microsoft Dynamics AX
- Creation of sales reports in Power BI
- Training employees in business intelligence
- Technologies: Power BI, SQL, SQL Server Integration Services (SSIS)
Nikola N.
Last position:
Founder and CEO - Senior Business Analyst at fintechno AI GmbH
- Business analysis
- Requirements engineering
- System design
- Various insurance projects
- Technology stack: msg.Billing (InExkasso)
- Product: Pay-per-use car insurance based on blockchain and OBD-II real-time telemetry with AI
Stefania D.
Last position:
Data Engineer at Storebox
Tech: AWS (Glue, Lambda, Redshift), Airflow, PostgreSQL, Python, PySpark, Metabase, Power BI
Delivered: Analytics-Ready Data Models • Legacy SQL to Cloud ETL Migration • Dynamic Pricing Engine
- Owned and evolved the company data warehouse end-to-end — from ingestion to transformation to analytics-ready dimensional data models on AWS Redshift.
- Collaborated with Analysts, Data Scientists, and business stakeholders to deliver scalable dimensional data models that enable self-serve analytics and streamline dashboarding in Metabase and Power BI.
- Architected end-to-end ETL/ELT pipelines on AWS (Glue, Lambda, Redshift) using Python and PySpark, orchestrated with Apache Airflow (MWAA) for reliability and observability.
- Defined and enforced data quality standards and governance practices across pipelines and the core data layer.
- Led migration of legacy SQL infrastructure into scalable AWS Glue pipelines with distributed PySpark processing, eliminating bottlenecks and reducing downtime.
- Developed a dynamic pricing engine applying automated promotional discounts based on occupancy rates, competitor pricing, and location performance tiers.
- Designed schema mappings to ingest MongoDB data into structured relational systems (Redshift/PostgreSQL).
Laura H.
Last position:
Sr. BI Consultant & Project Manager at Multivariata
- Project Lead, Sr. BI Expert, Data Scientist, Automation Engineer for Marketing Analytics | Customer Insights (retail, e-commerce)
- Product Manager: Asset Metadata Ingest & Provide
- Product Owner Business Intelligence – responsible for 2 teams: cloud data engineering, data warehouse (logistics)
- Product Owner for setting up a reporting platform on Azure & Power BI (finance)
Anton L.
Last position:
Self-employed at Controlling Lab eU
Business intelligence and digitalization projects
Controlling as a Service
Financial accounting and payroll accounting
Interim management
Business Intelligence Competence Team (2025-2026) at a food retailer (21 Bn. revenue, 52k employees): Project work at the interface between the finance department and IT service provider; various implementation projects in reporting and business intelligence
Consulting services (2025-2026) for a biotech startup (3 Mio. revenue, 20 employees): Optimization and automation of accounting and controlling processes; introduction of a central data warehouse system for planning and reporting
Consulting services (2025-2026) in the broadcasting and telecommunications industry (130 Mio. revenue, 200 employees): Analysis and troubleshooting of financial processes in the BSS system (customer management and billing system)
Consulting services (2024) in the broadcasting and telecommunications industry (130 Mio. revenue, 200 employees): Migration and implementation of cost accounting in a new database system
Consulting services (2025-2026) for advertising/marketing agencies (40 Mio. revenue, 450 employees): Establishment of group-wide cash management in the holding company, liquidity planning, receivables management and consolidation
Fabio G.
Last position:
IT Architect, Requirements Analyst and Consultant at CANCOM
- Supports CANCOM customers in migrating legacy on-prem systems to Microsoft Fabric and Microsoft Foundry
- Takes over and stabilizes existing solutions after a short handover
- Business analysis and requirements engineering for migration to a new cloud environment
- Optimization of machine learning models for feature extraction and customer profiling
- Ensures data protection and compliance
- Leads the migration of on-prem systems to Microsoft Fabric
- Designs new AI platforms for clients
- Tests the integration of chatbots for document intelligence with Microsoft Foundry, including requirements analysis, implementation, validation, and client communication
Frank W.
Last position:
Software Development at REI and EMI
- Code review, bug fixing, and new development of program code in PL/SQL and Oracle Forms
- Migration of the database version to Oracle 12/19 and migration of the Oracle Forms environment from client-server to web (6i → 12c)
- Fixing blocking sessions and implementing new workflow requirements
- Use of PL/SQL packages, procedures, functions, database triggers, batch files, and Java applications
- Revision of screen masks based on Oracle Forms and client-side PL/SQL code
- Business handling of issuing and processing vouchers (REI = Reimbursement; EMI = Emission), including billing with acceptance partners
- Agile development based on SCRUM with monthly sprints and weekly coordination meetings
Michael G.
Last position:
Scrummaster at Xenovo
Planning and monitoring sprints and holding the scheduled meetings
Analyzing and resolving issues in the development process
Error analysis, bug management and correction in individual process steps
Stakeholder management
Documentation and improvement of the development process
Tools: JIRA, Confluence
Lorenz G.
Last position:
Business Analyst at Wirtschaftsagentur Wien
- Conducting a feasibility study on introducing an in-house DWH as a central data source
- Conducting workshops for current state analysis (processes, reports, KPIs) together with the clients
- Gathering and detailing business requirements including a target concept for an in-house DWH
- Coordinating and clarifying data deliveries and interfaces in meetings with stakeholders and data providers
- Developing initial data models as a basis for data quality and later implementation
- Drafting solution variants including architecture and operation options and decision basis
- Creating the business case including effort estimates, cost-benefit analysis, and decision report
Benjamin A.
Last position:
Multi-Project Manager at Trading Company
- Setup of a new Data Warehouse (Budget ~€15M 2025 – 2026)
- Backend modernization project (Budget ~€5M 2025 – 2026)
- Standard software rollout with custom programming (Budget ~€9M 2025 – 2026)
Discover over 15,000 top freelancers
Statistics of experts using Data Warehouse
Aggregated from the professional profiles of matched freelancers.
Experience
20 years
Position duration
2.7 years
Positions per freelancer
12
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
100%
Master's degree or higher
91%
Doctorate
9%
Certifications per freelancer
4
Most common languages
German, English, French
Speak two or more languages
96%
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 Vienna 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 Vienna 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 29 Oct 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 (88%)
- Banking and Finance (72%)
- Professional Services (44%)
- Telecommunication (44%)
- Transportation (36%)
- Manufacturing (36%)
- Government and Administration (36%)
- Education (32%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
A foundation for analytics
A data warehouse collects, organizes and serves information for reporting, business intelligence and advanced analysis. It separates analytical workloads from operational systems, so teams can query consistent data without slowing core applications. Modern warehouses may run in the cloud, on company infrastructure or across both.
Models and pipelines
Strong professionals design dimensional, normalized or hybrid models around real business questions. They build ingestion and transformation pipelines, define reliable refresh schedules and document lineage from source to report. Work often includes batch processing, change-data capture, data quality checks and access controls.
Ecosystem and tooling
The work extends beyond the warehouse itself:
- Connect ERP, CRM, application, event and finance data
- Transform data with SQL, dbt or orchestration tools
- Operate Snowflake, BigQuery, Amazon Redshift or Azure Synapse
- Publish trusted datasets to Power BI, Tableau or Looker
Professionals also work with Python, cloud storage, APIs, monitoring and identity services. The right stack depends on data volume, latency, governance and existing architecture.
When companies need help
Companies bring in freelance expertise when reporting definitions conflict, pipelines fail silently or a legacy warehouse cannot support new analytical workloads. Specialists are also valuable during a cloud migration, a merger, a new product launch or the rollout of self-service analytics. In Vienna, remote collaboration can work well, while workshops with local business and technology teams may benefit from on-site availability and German or English communication.
Deliverables that matter
Typical outcomes include a warehouse architecture, source-to-target mappings, tested transformation models and documented data contracts. Experts may also deliver a migration plan, cost controls, dashboard-ready marts, automated quality tests and operational runbooks. Every deliverable should make ownership, freshness, definitions and failure handling clear.
What strong experts bring
The best professionals combine SQL depth with sound data modeling and a practical understanding of business processes. They ask how metrics are defined before building tables, challenge unreliable sources and make trade-offs visible. Look for experience with governance, performance tuning, security, observability and stakeholder training, not just familiarity with a named warehouse product.
Frequently asked questions
What clients ask us most about Data Warehouse — answered in short.
A Data Warehouse brings together information from operational systems so companies can analyze performance, finance, customers and processes in one consistent environment. It supports reporting, dashboards, forecasting and other analytical workloads without placing the same pressure on transaction systems.
A data warehouse usually stores structured, modeled information that is ready for reliable analysis, while a data lake can retain raw structured, semi-structured and unstructured data. Many modern architectures use both, with a lake for flexible storage and a warehouse or lakehouse layer for governed consumption.
A Data Warehouse specialist should understand SQL, dimensional modeling, data integration and pipeline orchestration. Experience with dbt, Python, cloud storage, BI tools, data governance, testing and observability is also useful when the work spans the full analytics lifecycle.
The right level depends on the complexity of the sources, governance needs, migration scope and number of teams involved. A strong data warehouse professional can assess the current architecture, identify risks and propose a delivery plan before implementation begins.
Yes, much of data warehousing can be delivered remotely through version control, cloud consoles, documentation and structured workshops. Vienna-based projects may still benefit from occasional on-site sessions when source-system owners, business stakeholders or security teams need close coordination.
A cloud data warehouse should be selected according to workload patterns, existing cloud services, governance, data location, integration needs and cost controls. Snowflake, BigQuery, Amazon Redshift and Azure Synapse each suit different operating models, so product preference should follow the architecture rather than replace it.
Ask a Data Warehouse professional to explain how they would validate source data, define business metrics, test transformations and monitor pipeline failures. Good answers connect technical choices to data ownership, user needs, security and long-term maintenance.
A data warehousing engagement may include architecture documentation, source mappings, transformation models, automated tests, access rules and dashboard-ready data marts. It should also leave clear runbooks, lineage information and ownership details so the company can operate the environment after handover.
The average hourly rate of freelancers in Vienna, Austria 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 in Vienna, Austria who have used Data Warehouse in their recent projects, 100% hold at least a Bachelor's degree, 91% hold at least a Master's degree, and 9% hold a doctorate.
On average, freelancers in Vienna, Austria who have used Data Warehouse in their recent projects have 20 years of professional experience, with a single engagement typically lasting around 2.7 years.
The most common languages among freelancers in Vienna, Austria who have used Data Warehouse in their recent projects are German (100%), English (96%), and French (32%).
The most common industries among freelancers in Vienna, Austria who have used Data Warehouse in their recent projects are Information Technology (88%), Banking and Finance (72%), and Professional Services (44%).
The most common business areas among freelancers in Vienna, Austria who have used Data Warehouse in their recent projects are Information Technology (100%), Business Intelligence (84%), and Project Management (80%).
Main locations of FRATCH Experts, who have recently used Data Warehouse
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.
Countries:
Vienna 
Graz Request a free demo
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Philipp Thomaschewski
FRATCH CEO
