Data Warehouse Experts in Austria
, matched in minutes with vetted, available freelancersHire experts who design scalable data models, build reliable ELT pipelines and connect cloud platforms such as Snowflake, BigQuery or Microsoft Fabric. FRATCH matches you quickly and precisely with vetted, available freelancers for your data warehouse project.
Meet FRATCH Experts in Austria, who have recently used Data Warehouse
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
Alexander P.
Last position:
Owner & Lecturer at Own company for AI governance and data products, Vienna
- Consulting and interim management at the interface between IT operations and regulation
- Impact analysis 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
- Lectures in Data & Information Management and Human-Machine Interaction at University of Applied Sciences Burgenland, since 2023
- Supervision of master’s theses and participation in the examination board
- Presentations for business 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
- MLOps and AIOps in live operations
- Own applications under own brand: shared codebase, separate delivery for each target device
- AI-assisted software development (vibe coding), complete agentic pipelines, code generation, implementation, automated testing, CI/CD and release cycles
- Publications on the EU AI Act, NIS2, DORA, CRA and CER as an integrated governance system
- 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 / Sales Transformation at SEFE
- Organizational Change Management as part of a group-wide, supranational sales transformation, serving as the core of a broad restructuring of business-critical areas, with end-to-end control of the change process and long-term embedding and securing the future readiness of sales.
- Initiation and end-to-end steering of the sales transformation program, including in-depth analysis to identify, assess, and strategically involve all relevant stakeholders, making tactical use of their specific business interests and influence.
- Conducting advanced, detailed change impact analyses to predict the organizational effects of the transformation, deriving a tailored change strategy, and developing a change roadmap and a change story as the core narrative of the campaign.
- Designing and implementing an integrated communication and action plan to ensure maximum transparency, effectively manage unspoken expectations, and actively guide a change process that is clear and understandable for all stakeholders.
- Active management of all transformation phases, including the setup of robust monitoring mechanisms for continuous progress analysis and forecasting based on recommended and jointly defined KPIs.
- Establishing an agile analysis and adjustment cycle to continuously assess the effectiveness of implemented measures, identify challenges early, and fine-tune the change strategy in time.
- Conducting precise gap analyses to identify skill gaps in the sales team in the context of the new strategic direction, and deriving and developing targeted qualification measures (e.g. coaching, training).
- Comprehensive reduction of resistance to change by turning opposing stakeholder mindsets into proactive change-driving change ambassadors.
- Deep and sustainable realignment of sales structures and processes in full alignment with the company’s overall goals.
- Qualitative and quantitative improvement of sales efficiency and performance as a direct result of the implemented transformation initiatives and capability-building programs.
Karl F.
Last position:
Managing Director at ONECEPT GmbH
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)
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).
Immo M.
Last position:
Interim Manager/Management Consultant at Austrian regional energy supplier
Further development of the data analytics system landscape based on MS Power BI
- Concept and development of ETL processes, primary data source SAP IS/U incl. extraction via Theobald extractors to Azure Synapse, support with SAP S/4 conversion
- Support in the concept and development of data models in Azure Synapse Analytics
- Gathering and implementation of requirements in the data analytics area from internal business units
- Identification, documentation and analysis of existing as well as cross-functional processes
- Data definition, data modeling & preparation as well as source selection for merging data from differently structured data sources
- Design of reports and dashboards based on MS Power BI
- Transfer of reporting processes from existing structures to MS Power BI Premium (PPU)
- Hands-on development and implementation of analyses, reports and dashboards based on Microsoft Power BI
- Power Apps for write-back processes from Power BI Premium to Azure SQL DB
- Power BI support as part of the SAP S/4 conversion
- Cooperation with internal business units and external development partners
- Coaching/training, documentation, development processes
Elija L.
Last position:
Fujitsu Germany
- Supporting and helping shape the setup and further development of a company-wide HUB system in the Fujitsu environment for integrating a wide range of data sources and applications in an international context
- Designing and implementing data modeling, ETL processes, reporting, and data quality management
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
Christian P.
Last position:
Migration Lead for Accounting & Year-End Closing Tests at Raiffeisenverband Salzburg
- Project and test management for the accounting migration
- Coordination of resources
- Planning, creation and alignment of scenarios with functional experts and IT product owners
- Responsibility for accounting KPIs of the migration cockpit
- System environment: Jira, XRay, Confluence
Hossein A.
Last position:
Datawarehouse Consultant at LENZING AG
- Consulting on the enterprise data-warehouse and reporting practice at one of Austria's largest industrial groups.
- Designing and standardizing Power BI dashboards and data-visualization governance for company-wide enterprise reporting.
- Developing a WCAG-compliant, colorblind-safe visualization standard (Okabe-Ito palette) to harmonize dashboards across the organization.
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
Nikolaus J.
Last position:
Integration Architect at CECIL
Designed an API-first omnichannel integration, linking WhatsApp Business API with Salesforce Marketing Cloud to unify customer data across multiple platforms.
Automated customer onboarding and engagement workflows using Marketing Cloud Journeys, SSJS, and Azure Functions.
Developed a middleware layer to sync WhatsApp interactions with Sales Cloud for seamless customer experience tracking.
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
22 years
Position duration
3.2 years
Positions per freelancer
12
Top business areas
Information Technology, Business Intelligence, Project Management
Top industries
Information Technology, Banking and Finance, Education
Certification focus areas
Information Technology, Project Management, Business Intelligence
Bachelor's degree or higher
90%
Master's degree or higher
77%
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 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Austria are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Austria 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 19 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 (84%)
- Banking and Finance (65%)
- Education (41%)
- Manufacturing (41%)
- Professional Services (41%)
- Telecommunication (41%)
- Transportation (35%)
- Retail (32%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What a Data Warehouse Does
A data warehouse consolidates structured data from operational systems into a reliable environment for reporting, analysis and decision-making. It separates analytical workloads from transactional applications and gives teams consistent definitions for revenue, customers, products and performance. Modern warehouses may run in the cloud or within a controlled private infrastructure.
Core Architecture
Strong specialists shape the warehouse around business questions, data volume, latency and governance needs. They select dimensional, normalized or Data Vault models, define ingestion patterns and design layers for raw, transformed and trusted data. They also plan storage, compute, access controls, lineage and recovery so the system remains maintainable as sources change.
Platforms and Tooling
The ecosystem includes cloud warehouses such as Snowflake, Google BigQuery, Amazon Redshift and Microsoft Fabric, alongside established technologies such as Teradata and Oracle. Specialists commonly work with SQL, Python, dbt, Apache Airflow, Fivetran, Matillion and cloud storage. They connect ERP, CRM, finance, product and event data while preserving clear ownership and documentation.
Typical Delivery Work
- Assess source systems, reporting gaps and data quality risks
- Design schemas, marts, naming rules and transformation layers
- Build batch or near-real-time ingestion and ELT pipelines
- Implement tests, monitoring, lineage and access policies
- Tune workloads and migrate legacy warehouse environments
These deliverables support financial reporting, customer intelligence, supply chain analysis, marketing attribution and operational dashboards. The right design makes trusted data available without forcing every team to interpret raw source tables.
When Companies Need Support
Companies bring in freelance expertise during a warehouse migration, a new analytics program, a merger or a major change in source systems. Warning signs include conflicting reports, manual spreadsheet preparation, slow dashboards, undocumented pipelines and recurring reconciliation work. In Austria, external specialists can work remotely or on site with internal data, finance and IT teams, provided communication, access and language expectations are agreed early.
What Strong Specialists Bring
A capable professional combines SQL depth with data modeling, orchestration, cloud security and practical knowledge of analytical workloads. They clarify metrics with stakeholders, challenge weak assumptions and make trade-offs visible. Quality shows in tested transformations, reproducible deployments, useful documentation, controlled costs and monitoring that detects silent data failures before business users do.
Frequently asked questions
Everything clients usually want to know about Data Warehouse, in one place.
A Data Warehouse brings together data from systems such as ERP, CRM, finance and e-commerce applications for analysis and reporting. It supports governed metrics, dashboards, forecasting and historical comparisons without putting analytical pressure on transactional databases.
A Data Warehouse usually stores curated, structured data that is ready for consistent business analysis. A data lake keeps broader raw or semi-structured data and offers more flexibility, but it needs strong cataloging and transformation practices to remain useful. Many organizations use both in a lakehouse or layered architecture.
A Data Warehouse specialist should often understand SQL, dimensional modeling, ELT, orchestration and data quality testing. Experience with cloud storage, identity management, infrastructure as code, BI tools and source-system integration is also valuable. Familiarity with dbt, Apache Airflow or equivalent tooling can improve delivery.
A Data Warehouse project needs enough experience to cover its source systems, security model, reporting goals and operating environment. A focused migration may need a specialist with deep platform knowledge, while a broad enterprise program benefits from someone who can align architecture, governance and stakeholder needs. Scope and risk matter more than a fixed experience label.
A Data Warehouse engagement can work remotely when access, documentation and decision-making routines are clear. On-site collaboration may help during source discovery, metric workshops or sensitive handovers. For teams in Austria, agree on working language, availability, security controls and meeting cadence before the engagement starts.
Snowflake offers separated storage and compute with broad data-sharing features, while BigQuery emphasizes serverless analytics and Redshift fits closely with the AWS ecosystem. The right choice depends on workload patterns, existing cloud services, governance, skills and cost controls. A strong specialist should explain these trade-offs using your requirements rather than promote one platform.
A Data Warehouse implementation should have tested transformations, documented ownership, clear metric definitions and traceable lineage. Reliable refresh monitoring, controlled access, repeatable deployments and useful failure alerts are equally important. Business users should be able to understand where a key figure comes from and trust its refresh status.
A Data Warehouse assignment often involves incomplete source documentation, changing definitions and coordination with finance, IT, analytics and business teams. Freelance specialists may design models, stabilize pipelines, migrate platforms or establish governance practices. Success depends on making assumptions explicit and leaving behind maintainable processes, not only delivering queries.
The average hourly rate of freelancers in Austria who have used Data Warehouse in their recent projects is 110 €, which corresponds to a daily rate of about 879 € based on an 8-hour working day.
Of the freelancers in Austria who have used Data Warehouse in their recent projects, 90% hold at least a Bachelor's degree, 77% hold at least a Master's degree, and 10% hold a doctorate.
On average, freelancers in Austria 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 in Austria who have used Data Warehouse in their recent projects are German (100%), English (95%), and French (27%).
The most common industries among freelancers in Austria who have used Data Warehouse in their recent projects are Information Technology (84%), Banking and Finance (65%), and Education (41%).
The most common business areas among freelancers in Austria who have used Data Warehouse in their recent projects are Information Technology (100%), Business Intelligence (89%), and Project Management (65%).
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.
Vienna Graz 
Vienna Countries:
- Germany
- Austria
- Switzerland
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FRATCH CEO
