Analytics Engineers in Germany
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Meet FRATCH Analytics Engineers in Germany
Ajay Kumar Deekonda
Last position:
Senior BI and Analytics Engineer at Novartis
- Led enterprise reporting modernization by migrating legacy SSRS reporting solutions to Power BI, supporting 500+ business users while ensuring full GDPR/DSGVO compliance.
- Designed and optimized Power BI and Microsoft Fabric semantic models using star schema, dimensional modeling, advanced DAX, and performance optimization techniques, reducing query latency by 25%.
- Delivered 20+ executive and operational dashboards featuring KPI scorecards, drill-through, bookmarks, and row-level security, improving reporting efficiency by 20%.
- Enabled self-service analytics through governed Power BI datasets, dataflows, and gateway architecture, increasing business-led reporting adoption by 35%.
- Configured an incremental refresh policy and query folding for a 50+ million row sales dataset, reducing daily report refresh times by 85%.
- Deployed automated ETL/ELT pipelines using Azure Data Factory, Microsoft Fabric, and Snowflake, reducing reporting delivery timelines by 40% through workflow automation.
- Spearheaded Microsoft Fabric analytics modernization initiatives including lakehouse architecture, OneLake integration, and centralized data platform development, reducing data latency from 2 hours to 20 minutes.
- Translated business requirements from 15+ stakeholders into scalable Power BI semantic models and dashboards, improving reporting consistency and reducing ad-hoc reporting requests by 25%.
- Applied Microsoft Copilot and generative AI tools to accelerate SQL development, DAX authoring, technical documentation, and testing activities, reducing development effort by approximately 15 hours per week.
Oliver Fries
Last position:
Modernization of a multi-company backend system at Energy utility company
Enhancement and modernization of a mature Aspire backend application in the environment of a utility company, focusing on new business requirements, testing, legacy code cleanup, and stable backend delivery.
Core contributions & results Implemented new business requirements in the context of customer orders, subcontractors, and cross-company backend processes, and ensured consistent workflows in a distributed system landscape. Modernized existing backend components step by step and reduced technical debt through targeted legacy code cleanup, refactoring, and structured code reviews. Improved the testability of business-critical services by expanding automated tests with xUnit, AutoFixture, and clearer validation structures. Supported the further development of workflow automations and integration processes via microservices, messaging, and API-based communication. Took over source code from external firms, systematically checked code quality, and derived technical improvements for maintainability, stability, and integration. Worked in agile development processes with Jira, Confluence, and Azure DevOps and supported cross-team alignment on architecture, quality, and implementation. Technical metrics Technologies & methods C#, .NET, ASP.NET, ASP.NET Core, Aspire, Docker, RabbitMQ, gRPC, REST API, Swagger, Microservices, NServiceBus, AutoMapper, Autofac, xUnit, AutoFixture, FluentValidation, Entity Framework Core, MediatR, Redis, Consul, Serilog, SonarQube, Azure DevOps, Azure Monitor, GitLab, Google Protocol Buffers, IronPDF, Mailjet, Jira, Confluence, Miro, agile development, Scrum, code reviews, refactoring, legacy code cleanup, workflow automation, power grids
Chisom N.
Last position:
Founder & Analytics Engineer at Museni Nexus
- Client — Podimo ApS (podcast & audiobook streaming): build the finance reporting layer on BigQuery + dbt + Airflow, including the core revenue-transaction fact tables used across finance reporting.
- API automation: design and build a BigQuery → Airflow → Microsoft Dynamics 365 Business Central REST-API pipeline to automate sales-invoice posting, with idempotency and master-data sync between systems.
- Delivery: sole engineer on the engagement — requirements, modelling, orchestration and stakeholder communication with the client finance team, end to end.
Syed Muhammad Aun Raza Zaidi
Last position:
Master Thesis - Semantic Layer at Linde GmbH, Linde Engineering - Commercial Department
- Thesis Title: Designing a Semantic Layer for Enterprise Analytics and AI Integration.
- Designed a semantic layer architecture for enterprise analytics that improved dataset discovery, relationship mapping, and governed access to reporting data.
- Built schema discovery and SQL generation workflows across 400+ data products, translating complex data structures into consumable analytics assets.
- Implemented role-based access control, Row-Level Security, and query validation to ensure data quality, governed access, and reliable use of enterprise reporting datasets.
Mirza Avdibegovic
Last position:
Consultant Data Warehouse at InfoFabrik GmbH
- Data Management of Oracle based Data Warehouse
- Operational monitoring of DWH
- ETL Processes in Oracle SQL
- Consulting in reporting and DWH development
- Developing Dynamic List, Grid, Jxls, Pivot Reports in Report Server
- Data visualization in Report Server
Emmanouil Tzouridis
Last position:
Senior Analytics Engineer at Trade Republic Bank GmbH
- Implementation of analytics and automation solutions for the Anti Financial Crime business unit
- Providing the infrastructure, including reusable data models and feature ingestion for production ML and rule based models in the areas of Account Take-Over and Card fraud detection, as well as Customer Risk Assessment
- Tools used: Snowflake, dbt, Looker, AWS, Python, Airflow, Metaflow
Sara Zarei
Last position:
Data Analyst / Analytics Engineer at IDG Tech Media GmbH
- Designed, built, and maintained scalable ETL/ELT data pipelines using Python, SQL, REST APIs, AWS Lambda, S3, PostgreSQL RDS, EventBridge, CloudWatch, Docker, Apache Airflow, and BigQuery – integrating data from GA4, Google Ads, Meta Ads, CMS, CRM, newsletters, events, and B2C ordering systems into analytics-ready datasets.
- Built a cross-brand lakehouse architecture from AWS to BigQuery – transforming raw JSON/CSV data into structured, partitioned, and reusable reporting layers with staging, intermediate, canonical, and mart models.
- Designed relational and dimensional data models: 3NF staging models, star schemas, fact tables, dimension tables, daily KPI aggregates, and dashboard-optimized marts for marketing, content, subscription, event, CRM, and revenue analysis.
- Implemented production-grade data quality and pipeline reliability features: incremental loads, idempotent upserts, deduplication, schema validation, row matching, null checks, anomaly detection, freshness monitoring, logging, retries, and error alerts.
- Automated cross-brand reporting processes and data products – pipelines for 73 newsletter campaigns, 31 lead list syncs, 52 event partner reports, and a 500K-record company matching pipeline; reduced manual data preparation by approx. 70% and increased analyst productivity by approx. 30%.
Elnazossadat Hosseininia
Last position:
Data Analyst at Siemens Healthineers
- Developed KPI dashboards using Power BI and DAX for 4+ business units, improving reporting transparency and strategic decision support.
- Migrated enterprise finance data views into dbt models, implementing modular SQL transformations, version-controlled data pipelines, and automated documentation to create a scalable analytics layer.
- Built dimensional data models in Snowflake for enterprise finance data, enabling scalable forecasting and supporting executive decision-making.
- Designed end-to-end ETL/ELT pipelines using Snowflake and SAP HANA, integrating data from 3+ enterprise systems.
- Automated monthly reporting workflows using SQL and Power BI, delivering strong business impact by reducing manual effort by 80%.
- Collaborated with finance stakeholders to translate business requirements into analytical data models, supporting strategic decision-making cycles.
- Delivered ad-hoc financial reports using Power BI, reducing turnaround time by 60%.
- Implemented data validation logic in SQL, resolving 95% of recurring data quality issues.
Ahsan Javed
Last position:
Data Analytics Developer at Level Next Productions
- Built Power BI dashboards and enabled data-driven strategies across digital platforms
Discover over 15,000 top freelancers
Analytics Engineers statistics
Aggregated from the professional profiles of matched freelancers.
Experience
13 years
Position duration
2.5 years
Positions per freelancer
7
Top business areas
Information Technology, Business Intelligence, Operations
Top industries
Information Technology, Education, Manufacturing
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
100%
Master's degree or higher
89%
Certifications per freelancer
2
Most common languages
German, English, Urdu
Speak two or more languages
100%
Based on our profile pool as of 27 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this role in 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 for Analytics Engineers in Germany
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 27 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the role
What they deliver
An analytics engineer turns raw data into trusted tables that analysts and business teams can use without rework. The focus is on clean modeling, clear business logic, and data that stays stable when sources change.
- Build dbt models and layered warehouse structures
- Define metrics, dimensions, and reusable business logic
- Set up tests, documentation, and lineage for trust in data
- Improve performance in the warehouse and reduce duplicated SQL
- Support handover from data engineering to BI and reporting teams
Core skills
Strong analytics engineers know SQL deeply and understand how modern warehouses work. They can read source systems, translate business rules into models, and keep pipelines maintainable as the data stack grows.
- SQL and data modeling
- dbt and transformation workflows
- Snowflake, BigQuery, Redshift, or Databricks
- BI tools such as Looker, Power BI, or Tableau
- Testing, documentation, and version control
When to bring one in
Companies usually hire a freelance analytics engineer when reporting is slow, messy, or built on too many one-off queries. It is also a good fit when a data team needs help standardizing definitions or preparing for a new warehouse or BI rollout.
In Germany, this is common for software firms, industrial groups, e-commerce teams, and scale-ups that need tight cooperation with product, finance, or operations. Remote work is often enough, but workshops on site can help align business logic and ownership.
Typical projects
An analytics engineer may be asked to clean up a broken metric layer, build a new dbt project, or make finance and product dashboards use the same definitions. They often work close to data engineers, BI developers, and data analysts.
- Migrate SQL logic into reusable dbt models
- Standardize KPI definitions across teams
- Create a semantic layer for self-service reporting
- Document source-to-dashboard data flow
- Improve query speed and model quality
What strong freelancers do well
Good analytics engineers do more than write SQL. They ask where a number comes from, how it will be used, and who depends on it. They keep models simple, explain trade-offs, and leave behind code that another engineer can maintain.
A strong analytics engineer also knows when to push work back to the source system, when to model in the warehouse, and when a metric needs a formal definition before any dashboard is built.
Hiring signals
You likely need an analytics engineer if teams keep arguing about the same KPI, dashboards do not match, or analysts spend too much time fixing data instead of using it. You also need one when your data stack is growing faster than your model layer.
Freelance support works well for a migration, a model cleanup, or an urgent reporting backlog. It gives you focused execution without adding a long internal hiring process.
Frequently asked questions
Not sure where to start with Analytics Engineers? These answers cover the essentials.
An Analytics Engineer builds the transformation layer that turns raw warehouse data into stable, reusable models. The work usually includes SQL modeling, dbt projects, metric definitions, tests, and documentation. The goal is trusted data that analysts and business teams can use without rework.
Look for strong SQL, data modeling, and hands-on experience with dbt or a similar transformation workflow. Good analytics engineers also understand warehouses like Snowflake, BigQuery, Redshift, or Databricks, plus basic version control and testing. Just as important is the ability to translate business rules into clear models.
A analytics engineer sits between data engineering and BI. Data engineers focus more on ingestion, pipelines, and platform reliability, while BI developers focus more on dashboards and visual reporting. Analytics engineering owns the clean, tested model layer that makes reporting consistent.
A freelancer is a good fit when you need focused delivery for a model cleanup, a warehouse migration, or a reporting standardization effort. It also works well when the need is urgent or the scope is still changing. A freelance analytics engineer can start quickly and work alongside your existing data team.
Yes. Most analytics engineer work can be done remotely if the team has clear access to source systems, warehouse tools, and business context. In Germany, short on-site workshops can still help when teams need to align on KPI definitions or cross-functional ownership.
Typical deliverables are dbt models, tested transformation layers, metric definitions, and documentation of data logic. You should also expect clean handover notes and, when needed, guidance for analysts or BI developers. The best output is not just code, but a model layer your team can maintain.
Quality shows up in consistency, clarity, and maintainability. A strong Analytics Engineer writes models that are easy to read, easy to test, and hard to break when source data changes. If your team can trace a KPI from source to dashboard without confusion, that is a good sign.
Freelancers should expect close contact with product, finance, or operations teams, because metric definitions matter as much as code. A good analytics engineer asks sharp questions early and documents decisions clearly. That avoids rework later and keeps the model layer aligned with the business.
The average hourly rate for Analytics Engineers in Germany is 83 €, which corresponds to a daily rate of about 663 € based on an 8-hour working day.
Of the freelancers working as Analytics Engineers in Germany, 100% hold at least a Bachelor's degree and 89% hold at least a Master's degree.
On average, freelancers working as Analytics Engineers in Germany have 13 years of professional experience, with a single engagement typically lasting around 2.5 years.
The most common languages among freelancers working as Analytics Engineers in Germany are German (100%), English (100%), and Urdu (22%).
The most common industries among freelancers working as Analytics Engineers in Germany are Information Technology (67%), Education (44%), and Manufacturing (44%).
The most common business areas among freelancers working as Analytics Engineers in Germany are Information Technology (100%), Business Intelligence (89%), and Operations (56%).
FRATCH Analytics Engineers main locations
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 Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
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Philipp Thomaschewski
FRATCH CEO
