Azure Data Factory Expert in Frankfurt
for reliable data pipelines, matched in minutesHire experts who design Azure data integration workflows, connect cloud and on-premises sources, and deliver dependable ETL and ELT pipelines. FRATCH matches you quickly and precisely with vetted, available freelancers.
Meet FRATCH Experts in Frankfurt, who have recently used Azure Data Factory
Monika T.
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.
Carlbandro E.
Last position:
IT Lecturer / Coach at Freelance
As a freelance IT lecturer and IT coach, I specialize in teaching individuals concepts like Cloud Computing, Business Intelligence, Python Programming and Agile Frameworks. My goal is to simplify complex technical concepts into practical, actionable knowledge. Through my extensive experience as an IT consultant in various sectors and roles I know about the importance of IT training - for companies and employees alike.
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.
Ulm P.
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 Z.
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 K.
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 S.
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
21 years (Germany: 16 years)
Position duration
2.2 years (Germany: 2 years)
Positions per freelancer
15 (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, Project Management
Bachelor's degree or higher
100% (Germany: 96%)
Master's degree or higher
43% (Germany: 62%)
Doctorate
14% (Germany: 11%)
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 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Frankfurt 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 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 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Azure Data Factory 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 (100%)
- Energy (71%)
- Banking and Finance (71%)
- Healthcare (71%)
- Automotive (43%)
- Transportation (43%)
- Manufacturing (43%)
- Professional Services (43%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Azure Data Factory does
Azure Data Factory is Microsoft Azure’s managed service for moving, transforming and orchestrating data. Teams use it to build repeatable pipelines that connect databases, files, SaaS applications, data lakes and warehouses. The service supports both ETL and ELT patterns without requiring companies to manage the underlying integration infrastructure.
Core building blocks
ADF pipelines coordinate activities such as copying data, running transformations and triggering external processes. Datasets describe source and destination structures, while linked services store connection settings. Integration runtimes provide the execution layer for cloud, private network and hybrid scenarios, with parameters and variables supporting reusable designs.
Typical delivery work
- Connect SQL Server, Oracle, SAP, APIs, storage accounts and SaaS sources
- Build scheduled, event-driven and incremental data pipelines
- Transform data with Mapping Data Flows, SQL, Databricks or Synapse
- Load curated data into Azure Data Lake Storage, Synapse or reporting systems
- Add monitoring, alerts, retries and operational documentation
Ecosystem and adjacent skills
Strong ADF work often includes Azure Data Lake Storage, Azure Synapse Analytics, Azure SQL Database and Microsoft Fabric. Specialists may also use Azure Databricks, PowerShell, REST APIs, Git and Azure DevOps for transformation, automation and version control. Knowledge of SQL, data modelling, security roles and networking is important when pipelines cross cloud and private environments.
When companies need specialists
Companies bring in freelance expertise when a data platform must be modernised, a growing number of sources need one governed integration layer, or existing pipelines are slow and difficult to operate. Frankfurt teams may need on-site workshops for architecture and stakeholder alignment, while implementation, testing and monitoring can often be handled remotely. Clear communication in English or German can support collaboration across business and technical teams.
What strong professionals deliver
The best professionals start with data contracts, ownership and failure scenarios rather than simply connecting sources. They design for incremental loads, idempotent reruns, secure credentials and observable operations. They test edge cases such as schema changes and late-arriving data, control cloud costs, document dependencies and leave maintainable pipelines that internal teams can operate confidently.
Frequently asked questions
The facts hiring teams ask for most often when it comes to Azure Data Factory.
Azure Data Factory is used to build and operate pipelines that move and transform data between cloud services, private systems and business applications. Companies commonly use it for warehouse loading, lake ingestion, scheduled integration and hybrid data workflows.
ADF provides a broad, managed data integration service across many sources and destinations. Synapse pipelines use closely related capabilities inside the Synapse workspace, so the choice usually depends on whether integration is managed as a standalone service or as part of an analytics platform.
A strong Azure Data Factory specialist usually works comfortably with SQL, Azure Data Lake Storage, Azure Synapse Analytics and identity controls. Experience with Databricks, APIs, Git, Azure DevOps, networking and data modelling is valuable for larger or hybrid deliveries.
The right ADF experience depends on the scope, source complexity and production requirements. A straightforward data transfer may need focused pipeline skills, while a governed hybrid platform calls for professionals who can handle architecture, security, performance, monitoring and migration planning.
Azure Data Factory can connect to on-premises sources through a self-hosted integration runtime. The professional should understand network access, firewall rules, credential handling and operational ownership before designing the connection.
Yes, ADF delivery is often suitable for remote collaboration when access, documentation and decision paths are clear. Frankfurt companies may still prefer on-site sessions for discovery or workshops, with implementation and testing completed remotely in English or German.
Review how the Azure Data Factory professional handles incremental loading, retries, schema changes, security and monitoring, not just whether a pipeline runs once. Ask for clear runbooks, tests, alerting rules and an explanation of how failures can be investigated and safely repeated.
A maintainable ADF solution uses consistent naming, reusable parameters, controlled deployment and clear separation between configuration and logic. It also includes ownership details, dependency documentation, meaningful alerts and guidance for handling failed or delayed data loads.
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, 43% hold at least a Master's degree, and 14% hold a doctorate.
On average, freelancers in Frankfurt, Germany who have used Azure Data Factory in their recent projects have 21 years of professional experience, with a single engagement typically lasting around 2.2 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 (29%).
The most common industries among freelancers in Frankfurt, Germany who have used Azure Data Factory in their recent projects are Information Technology (100%), Energy (71%), and Banking and Finance (71%).
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 (71%).
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
