Azure Data Factory Expert in Frankfurt
with precise AI matching and vetted, available freelancersHire experts who design reliable data pipelines, connect cloud and on-premises sources, and orchestrate analytics workflows with Azure Data Factory, supported by fast, precise matching to vetted, available freelancers.
Meet FRATCH Experts in Frankfurt, who have recently used Azure Data Factory
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.
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.
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:
AWmOpsRtKekEX(CPEliRenIEtN: CInEfoSrs.yDs,aHtaitAarcchhiiEtencetr(gAyW) S)
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: 64%)
Doctorate
14% (Germany: 8%)
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 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Frankfurt 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 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 29 Oct 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 building, scheduling and monitoring data integration workflows. It moves and transforms data between cloud services, databases, files and on-premises systems without requiring a self-managed integration server. Teams use it to prepare trusted data for reporting, analytics, machine learning and operational processes.
Pipelines and transformations
A data factory solution combines pipelines, activities, datasets, linked services and triggers. Specialists configure copy activities for ingestion, mapping data flows for visual transformations, and integration runtimes for secure execution across environments. Parameters, variables and reusable templates help teams manage complex workflows without duplicating configuration.
- Ingest data from SQL databases, APIs, files and SaaS systems
- Transform and validate data before loading it into analytical stores
- Schedule, trigger and monitor dependent processing steps
- Reconcile source and target records with alerts and audit logs
Azure ecosystem
Azure Data Factory commonly works with Azure Synapse Analytics, Azure Data Lake Storage, Databricks, Azure SQL, Event Hubs and Power BI. Strong professionals also understand Azure Key Vault, managed identities, private endpoints and role-based access control. Infrastructure as code with Azure DevOps, ARM templates, Bicep or Terraform supports repeatable delivery across environments.
When companies need specialists
Companies often bring in freelance expertise when a migration must replace fragile scripts, when several source systems need one governed pipeline, or when existing workflows are slow and difficult to monitor. Support is also valuable during a move from SSIS to Azure Data Factory, a new lakehouse initiative, or a controlled handover to an internal team.
- Define source-to-target mappings and pipeline dependencies
- Set up secure connectivity for hybrid environments
- Improve failure handling, restart logic and operational visibility
- Document pipelines so internal teams can maintain them
What strong professionals deliver
Quality specialists begin with data contracts, ownership and clear acceptance criteria rather than only connecting endpoints. They design for incremental loading, idempotent reruns, schema changes, cost control and observable failures. Their deliverables may include deployment templates, parameterized pipelines, monitoring rules, runbooks and knowledge transfer.
Working with a Frankfurt team
Azure Data Factory work can be handled remotely, on site or through a mixed setup. For companies in Frankfurt, collaboration may involve local data, finance, logistics or industrial systems alongside Azure services, so clear communication with infrastructure, security and business teams matters. Confirm working hours, German or English language needs, access procedures and documentation standards before the engagement begins.
Frequently asked questions
The facts hiring teams ask for most often when it comes to Azure Data Factory.
Azure Data Factory is used to ingest, transform and move data between sources such as databases, files, APIs and Azure services. Companies use it to orchestrate repeatable workflows that feed data warehouses, lakehouses, reports and machine learning processes.
Azure Data Factory provides broad data integration and orchestration across many systems, while Synapse pipelines are closely integrated with Synapse workspace analytics. The right choice depends on whether the main need is shared integration across the Azure estate or an analytics workspace with pipelines managed alongside SQL and Spark resources.
A strong Azure Data Factory specialist often understands SQL, dimensional modelling, REST APIs, Azure Data Lake Storage and Azure Synapse Analytics. Knowledge of Databricks, Power BI, Key Vault, managed identities, networking and deployment automation is also useful for production work.
The required Azure Data Factory experience depends on the scope, source systems and security model. A simple scheduled transfer needs less depth than a hybrid migration involving incremental loading, complex transformations, private networking, deployment pipelines and operational support.
Azure Data Factory projects are often suitable for remote collaboration when access, documentation and communication routines are defined clearly. Frankfurt-based companies may still prefer on-site workshops for architecture, security reviews or handover, especially when local systems and several stakeholder groups are involved.
Azure Data Factory can run SSIS packages through its Azure-SSIS Integration Runtime, which helps teams move established integration workloads to Azure. It also supports newer pipeline and mapping data flow patterns, so specialists should assess whether to lift and shift packages or redesign them for managed cloud execution.
Review whether Azure Data Factory pipelines have clear ownership, parameterization, secure connections, useful monitoring and safe restart behaviour. Ask for examples of data validation, incremental processing, deployment automation, documentation and handling of schema or source-system changes.
A capable Azure Data Factory freelancer should provide tested pipelines, source-to-target mappings, linked service and trigger configuration, deployment templates and monitoring guidance. A runbook, access model, error-handling design and knowledge-transfer session make the solution easier for the internal team to operate.
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
