Azure Data Factory Experts in Frankfurt, Germany
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Meet FRATCH Experts in Frankfurt, Germany, who have recently used Azure Data Factory
Umut Gülac
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.
Monika Thepale
Last position:
Senior ETL Lead at Takeda GmbH
- Led design, development, and deployment of data solutions supporting a major pharma acquisition for Takeda Pharmaceutical Company, delivering transparency reporting systems across Azure,Databricks (Python and Shell Scripting) platforms.
- Owned,Designed and developed scalable ELT pipelines to process Customer and Product data using Azure, complex SQL, Databricks, and shell scripting, enabling efficient data integration and processing across multiple sources including job orchestration and workflow automation.
- Implemented performance optimization techniques (query tuning, parallelism, workload optimization), improving system efficiency and processing time.
- Applied strong analytical and problem-solving skills to assess technical solutions and support business requirements for compliance and transparency reporting.
- Designed scalable data foundations suitable for downstream analytics and AI workloads.
- Led data quality initiatives by assessing multiple source data, defining quality metrics, and establishing processes for monitoring and continuous improvement.
Ulm Paunel
Last position:
DataStage ETL Expert at ING Bank
- Datastage 11.7, dbt, Oracle 19, Python 3.12 / PySpark 3.5, Azure GitHub, Azure DevOps, Automic
- Development of migration jobs to transfer data from the collection DWH to the new Risk Mart, as well as development of ETL pipelines to migrate historical data from the old Mart to the new Risk Mart.
- Storage of the silver layer on Hadoop and the gold layer in Oracle.
- Translation of DataStage jobs into dbt to publish reporting data in Google Cloud to a PostgreSQL database.
- Creation and optimization of complex SQL queries for data extraction from a data vault, taking into account historical data in the point-in-time tables.
- Creation of Oracle table definitions (DDL) and adjustment of existing stored procedures.
- Versioning changes in GitHub and deployment via the CI/CD portal.
- Refactoring long-running DataStage jobs into Python using PySpark to reduce server load.
- Migration of SAS scripts to PL/SQL, including new development of distribution functions that have no direct equivalent in Oracle.
- Development of Automic jobs to run DataStage pipelines and Python scripts (PySpark jobs) that control the population of the SME and institutional risk tables in the Risk Mart and perform business calculations.
- Participation in the agile process, including creating user stories, estimations, and planning in Azure DevOps.
- Handling Azure DevOps tickets and close collaboration with testers and business teams for error analysis and resolution.
Ashkan Zadeh
Last position:
Microsoft Azure Senior Data Engineer / Senior Data Scientist at Vattenfall Europe
- Advising on the use of analytics and BI tools and services in the Microsoft Azure stack (e.g. MS Fabric, Synapse Workspaces and dedicated SQL pools, SQL Database, PostgreSQL, Snowflake, Databricks, Data Factory, SSIS, Analysis Services, Function Apps, Power BI, ML)
- Independently designing analytics solutions with Python, SQL, etc.
- Designing and implementing ETLs and data pipelines
- Creating and maintaining APIs
- Independently applying CI/CD, testing, and version control
- Data modeling
- Model development and optimization
- Anomaly detection with AI
- Predictive analytics
Used technologies:
- Snowflake
- Fabric
- Azure Synapse Analytics
- Azure DataFactory
- Azure Data Lake
- Azure DevOps
- Databricks
- Spark
- CI/CD
- SQL Database
- Python
- Power Platform
Petru Kisalita
Last position:
Architect & Technical Team Lead & Senior Developer at Goetel GmbH
- Design, architecture & development/programming of ETL/ELT data pipelines, DWH, BI solution
- Technical project lead, POC – proof-of-concept creation
- Liaison between business units and technical teams
- Azure DevOps Boards & Jira
- Data modeling & data engineering – data warehouse & data mart
- Azure (Data Factory, Azure SQL, Azure DevOps CI/CD, Azure Data Lake V2, Business Central REST API, OData API, OAuth2 tokens)
- SharePoint lists & API for ADF, Firebird DB, Postgres DB, DB2
- Power BI (Power Query), DAX, Excel PBI add-on, GIS data
- Automated ETL process monitoring/logging, performance monitoring, error monitoring – capturing & resolution
- Index performance tuning & statistics monitoring, Transact-SQL
- Data security – MFA (multi-factor authentication) & OAuth2, MS Graph, Azure networks & firewalls, gateways, roles, user groups – with read/write permissions
- Sources – Vario Bill, Camunda, Radius, Geo Database, OTRS, PAST, MS Dynamics Business Central, Azure Blob Data Lake, SharePoint lists
Ritika Solanki
Last position:
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Global marketing analytics for Hitachi Energy as part of a global data modernization initiative aiming to enhance data retention, historical data availability and provide Eloqua's 2-year retention for remote interaction reporting and analytics.
Analyzed Eloqua's default retention policy and identified risk of data loss for records older than two years.
Designed and implemented historical data preservation strategy by creating transformed tables in the target data platform to archive older data while ensuring data quality dashboards.
Collaborated with the Power BI team to re-point dashboards from raw Eloqua imports to the newly created archival layer.
Leveraged Jira to track and manage data engineering tasks, bugs, and feature requests across Agile sprints; coordinated backlog prioritization and task assignment to align data pipeline development with business needs.
Power BI dashboard optimization:
Worked closely with business stakeholders to assess and understand reporting needs for reverse customer data.
Designed and implemented incremental refresh in Power BI to ensure daily updates without full data reloads.
Collaborated with Azure data engineers to optimize data processing and publication pipelines.
Stakeholder communication & data modeling:
Acted as liaison between Group Data Office and Technology Office to align data modelling standards.
Gathered requirements from data engineering team and participated in weekly status meetings to provide implementation updates and resolve blockers across teams in Germany, Poland, and India.
Documentation & quality assurance:
Prepared end-to-end technical design documentation, data flow diagrams, and Power BI audit guides for future reference.
Participated in UAT sessions with business users to validate data outputs and report accuracy.
Discover over 15,000 top freelancers
Statistics of experts using Azure Data Factory
Aggregated from the professional profiles of matched freelancers.
Experience
23 years (Germany: 16 years)
Position duration
2.3 years (Germany: 2 years)
Positions per freelancer
16 (Germany: 11)
Top business areas
Business Intelligence, Information Technology, Project Management
Top industries
Information Technology, Energy, Banking and Finance
Certification focus areas
Information Technology, Business Intelligence, Human Resources
Bachelor's degree or higher
100% (Germany: 95%)
Master's degree or higher
33% (Germany: 60%)
Doctorate
17% (Germany: 10%)
Certifications per freelancer
4
Most common languages
German, English, French
Speak two or more languages
100% (Germany: 96%)
Based on our profile pool as of 30 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Frankfurt, Germany 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 Frankfurt, Germany using Azure Data Factory
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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
Pipeline Orchestration
Azure Data Factory is used to move, prepare, and orchestrate data across cloud and on-prem systems. It is a fit for batch ETL, ELT, and scheduled data workflows where reliability matters more than custom code.
Core Capabilities
- Build pipelines for ingestion, transformation, and delivery
- Connect SQL databases, APIs, files, and Azure storage
- Use triggers, parameters, and monitoring for repeatable runs
- Integrate with Synapse, Databricks, and Key Vault
Common Use Cases
Companies bring in Azure Data Factory specialists when they need new reporting flows, legacy data migration, or a clean setup for analytics platforms. In Frankfurt, this often supports finance, logistics, and enterprise teams that run mixed cloud and data estate environments.
What Strong Specialists Do
Good professionals think about source quality, lineage, error handling, and cost-aware design. They know how to keep pipelines maintainable, secure, and easy to hand over to internal teams.
Typical Delivery Work
- Build incremental loads and scheduled refreshes
- Map source-to-target transformations and copy activities
- Set up CI/CD, environment promotion, and alerting
- Tune performance for large transfers and complex dependencies
Adjacent Skills
Strong Azure Data Factory experts usually also know Azure Synapse, Databricks, SQL, Power BI, and Azure DevOps. They understand data modeling, access control, and how to troubleshoot connectors, credentials, and runtime failures across the Azure stack.
Frequently asked questions
The facts hiring teams ask for most often when it comes to Azure Data Factory.
Azure Data Factory is used to move and orchestrate data between systems. Teams use it for ETL and ELT pipelines, scheduled imports, and repeatable data preparation for reporting or analytics. It is especially useful when the work spans multiple sources such as SQL databases, files, APIs, and Azure storage.
Azure Data Factory is the full product name, and ADF is the common short form searchers use. People may also say Azure Data Factory when they mean the pipeline service, the orchestration layer, or the broader Azure data integration setup. In older conversations, some teams still compare it with SSIS-style workloads when planning a migration.
A Azure Data Factory specialist is a good fit when pipelines need to be built quickly, repaired after failures, or redesigned for scale and maintainability. Companies also bring in freelancers for migrations from legacy ETL tools, Azure landing zone work, or a short review before a production release. If the internal team is busy with analytics or application work, outside support can keep delivery moving.
Azure Data Factory focuses on orchestration and data movement, while SSIS is usually tied to older integration patterns and Databricks is stronger for large-scale transformation logic. Synapse often comes into the picture when teams want a broader analytics platform around the pipelines. A strong specialist knows when ADF should orchestrate work and when the transformation should happen elsewhere.
A strong Azure Data Factory freelancer usually knows SQL, Azure storage, Key Vault, monitoring, and CI/CD practices. Familiarity with Synapse, Databricks, Power BI, and source-control workflows helps a lot because real projects rarely stay inside one service. Security, retry logic, and clear error handling matter as much as pipeline design.
A Azure Data Factory project may only need a focused specialist for a straightforward pipeline build, but complex migration or multi-environment setups need deeper experience. The harder the source systems, approval flows, and deployment process, the more valuable a seasoned expert becomes. A good sign is whether the person can explain runtime choices, data flow logic, and failure recovery clearly.
Yes, Azure Data Factory work is often done remotely because most tasks live in Azure, source control, and documentation. Frankfurt-based teams may still prefer some overlap for workshops, access reviews, or stakeholder sessions, especially in regulated or cross-functional environments. Clear communication in English is usually enough, though German can help in local coordination.
Look for a Azure Data Factory professional who can explain pipeline design, monitoring, naming standards, and how they handle failed runs. Good answers mention triggers, parameters, linked services, secure secrets handling, and promotion between environments. The best people speak in concrete delivery terms, not only in service names.
The average hourly rate of freelancers in Frankfurt, Germany who have used Azure Data Factory in their recent projects is 95 €, which corresponds to a daily rate of about 762 € based on an 8-hour working day.
Of the freelancers in Frankfurt, Germany who have used Azure Data Factory in their recent projects, 100% hold at least a Bachelor's degree, 33% hold at least a Master's degree, and 17% hold a doctorate.
On average, freelancers in Frankfurt, Germany who have used Azure Data Factory in their recent projects have 23 years of professional experience, with a single engagement typically lasting around 2.3 years.
The most common languages among freelancers in Frankfurt, Germany who have used Azure Data Factory in their recent projects are German (100%), English (100%), and French (33%).
The most common industries among freelancers in Frankfurt, Germany who have used Azure Data Factory in their recent projects are Information Technology (100%), Energy (83%), and Banking and Finance (67%).
The most common business areas among freelancers in Frankfurt, Germany who have used Azure Data Factory in their recent projects are Business Intelligence (100%), Information Technology (100%), and Project Management (67%).
Main locations of FRATCH Experts, who have recently used Azure Data Factory
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.
Berlin Hamburg Munich Cologne 
Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg 
Munich Request a free demo
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Philipp Thomaschewski
FRATCH CEO
