Delta Lake Experts in Germany
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Meet FRATCH Experts in Germany, who have recently used Delta Lake
Fadi Shoaa
Last position:
Development of a production-ready Enterprise Document AI & Recommendation Platform at Freelancer
- Development of a production-ready Enterprise AI solution for the automated processing of invoices and business documents
- Integration of Azure AI Document Intelligence and LLM technologies into existing business processes
- Development of robust REST APIs for automated document processing and system integration
- Extraction, validation, and storage of structured invoice data in Azure SQL as a base for analytics and machine learning models
- Development of an AI-based recommendation engine with machine learning and deep learning to generate personalized product recommendations based on historical purchase data
- Implementation of logging, monitoring, error handling, and validation mechanisms for stable production use
- Collaboration with business teams to define business rules and integrate the solution into existing enterprise processes
Technologies: Python, Azure AI Document Intelligence, Azure OpenAI, Azure SQL Database, REST APIs, Machine Learning, Deep Learning, OCR, Pandas, JSON, Workflow Automation
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.
Alexander Bromberg
Last position:
Senior Data Engineer at RWE AG
Architected and maintained data products for renewable energy operations, covering wind turbine, grid-meter, and weather data. Built scalable ETL/ELT pipelines in Azure Databricks using Delta Lake (bronze/silver/gold layers) and processed data in various formats, including structured and semi-structured data. Contributed to a data quality framework supporting table and column documentation, outlier detection, and completeness metrics across all datasets within a data product. In addition, implemented a DORA KPI Databricks dashboard used across all data products. Optimized CI/CD processes in Azure DevOps to streamline deployment across development, test, and production environments.
Technology stack: Azure Databricks, PySpark, SQL, Delta Lake, Unity Catalog, Azure Data Lake, APIs, Dremio, Azure DevOps, YAML, Git, Databricks Workflows, Application Insights, Terraform, OpenAI API, Codex, LLM-assisted workflows
Jorge Machado
Last position:
Technical Lead / Fractional CTO at Würth GmbH
I designed and developed an AI-powered multi-tenant platform on Azure that transforms SAP process recordings into technical documentation, presentations and automated tests, processing over 15,000 process recordings for enterprise customers like Würth. I owned the architecture, the production releases and the DevOps setup. I also designed a multi-tenant system with SSO and role-based access on Azure. Implemented an MCP Server with Dynamic OAuth Authentication.
Main Tasks:
- Sprint planning and feature preparation
- Design the multi-tenant platform architecture (FastAPI, SQLAlchemy, PostgreSQL row-level security for tenant isolation)
- Develop AI pipelines with Prefect for transcription (Azure Speech API), document generation and SAP screen-recording analysis (Claude, gpt-4-mini)
- Design and implement an MCP server to expose tenant knowledge to LLM clients (Claude), with async retrieval and reranking
- Implement LLM cost tracking, rate limiting and client pooling for Anthropic/OpenAI/Azure OpenAI endpoints
- Set up CI/CD: Docker images to Azure Container Registry, GitHub Actions, Azure Static Web Apps, Alembic migrations in containers
- Manage production releases and execute live data migrations for enterprise customers
- Define engineering standards and architecture patterns for the team
Environment: Azure / Azure Foundry / Python / FastAPI / Prefect / React / PostgreSQL
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.
Tobias Lewen
Last position:
Data Engineer at unitb consulting GmbH
Tasks: Design and operation of end-to-end cloud data platforms for enterprise clients in publishing and finance, including infrastructure automation, pipeline development, monitoring, and data quality.
Activities:
- Built multi-layer data architectures on Databricks (Apache Spark, Delta Lake), BigQuery, and GCP
- Fully automated cloud infrastructure with Terraform across 3 environments (DEV/STG/PRD)
- Developed automated data pipelines with Python, dbt, and GCP services for different data sources
- Built monitoring and alerting systems for real-time platform monitoring
- Implemented data versioning and quality checks at every layer
- Designed automated test and deployment pipelines in GitLab and Bitbucket
Achievements:
- 2× production data processing capacity, reduced spike response time from minutes to ≤15 s, server errors ≈ 0
- Replaced 3,000 lines of manual configuration with a reusable automation module for 7 customer domains, configuration errors to 0
- Delivered a complete end-to-end data platform at ~€10/month infrastructure cost
- Migrated 7 database tables with 0 downstream issues
- Removed 100% exposed credentials, eliminated external vendor dependency
- Delivered integration of 3 teams in 1 sprint
Marco Lindner
Last position:
Senior IT Consultant | Cloud Data Engineer | Infrastructure Architect at Hannover Rück SE
Built an enterprise data lakehouse platform on Azure Databricks
Developed production data pipelines and governance structures
Implemented private cloud infrastructures using Terraform
Introduced modern CI/CD standards in Azure DevOps
Implemented secure IAM and governance concepts
Developed scalable PySpark and Delta Lake frameworks
Supported self-service analytics and data product approaches
Provided architecture and platform consulting for enterprise data initiatives
Built a central DataHub architecture for insurance data
Integrated multiple subsystems into a lakehouse platform
Introduced data governance and data lineage
Supported modern analytics and reporting standards
Optimized data delivery for business and analytics teams
Serge Kalinin
Last position:
MLOps (machine learning operations) at REWE Digital GmbH
- It is like a startup within REWE, where we have to build a new forecasting system on Google Cloud Platform from the scratch. Although, officially my role is called MLOps, my actual tasks also include development of data processing pipelines (data engineering) and data scientists tasks such as feature engineering and model trainings.
- GCP: Terraform (tofu), Vertex AI (Kubeflow), Cloud Run, IAM, Google Cloud Storage, BigQuery, Artifact Registry
- Data engineering: Snowflake as the main data warehouse, Terraform, DBT for data model implementations
- CI/CD: GitLab. We have built a CI/CD pipeline that automates deployments of new releases up to production environment
Julia Sagert
Last position:
Senior Data Scientist / Consultant at Cloud Nation GmbH
Python, SQL, PySpark, Databricks, Databricks SQL, Delta Lake, dbt, Azure Data Lake Storage, Azure Machine Learning, Azure DevOps, Power BI, Git, MLflow
- Developed, validated, and optimized predictive analytics and classification models using Python (pandas), SQL, and modern ML frameworks.
- Performed data analysis, feature engineering, model validation, cross-validation, and stability analysis to ensure robust model quality and performance.
- Communicated model assumptions, results, uncertainties, and limitations to business units, management, and technical stakeholders.
- Built scalable data and machine learning workflows in cloud-based analytics environments using Databricks and Microsoft Azure.
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
Vili Dhamo
Last position:
Technical Lead, Data Engineer at Mercedes-Benz Consulting
- Optimized the data architecture (medallion) to better decouple processing stages and improve transparency and reproducibility
- Ensured technical quality of data processing in Databricks by introducing schema enforcement, data quality checks and a structured data architecture
- Orchestrated pipelines with Azure Data Factory
- Professionalized and automated the development and deployment process by integrating Git and GitHub Actions
- Led the Data Engineering team (3 members) in a functional role
- Conducted workshops to optimize and stabilize the data platform and the development process
- Collected and prioritized new requests, maintained the product backlog
- Technologies: Microsoft Azure (Data Lake, Data Factory), Databricks, Apache Spark (PySpark), Python, SQL, Git, Confluence, Power BI, Power Apps, Dataverse, MS SharePoint, Mural
Christian Schulz
Last position:
Data-Scientist/AI Engineer at The Marcom Engine GmbH & Co. KG
- Concept creation and implementing AI Agents in AWS Cloud
- Continuously alignment with stakeholders
- Collaborate with DevOps
- Technologies: Git, CI/CD (GitHub Actions), Python/ML, Streamlit, Deno/typescript, AWS SAM, AWS Bedrock, AWS Lambda, AWS Dynamo DB, AWS S3, AWS Event Bridge etc.
Nima Nooshi
Last position:
Co founding LLM Engineer at LLM Ventures
- Co-founded an AI venture focused on building production-grade LLM applications and agentic systems
- Designed and implemented multi-agent AI workflows for financial and trading applications
- Developed LLM-powered copilot architectures for portfolio analysis, trade management, and personalized user coaching
- Built on-device and edge-deployed inference applications, optimizing models for low latency, privacy, and resource-constrained environments
- Led system architecture decisions across model selection, orchestration, state management, and deployment
Jorge Machado
Last position:
Data Architect at Deutsche Bahn
- Design and provide best practices on data modeling for dbt, including changing dimensions, late arriving data handling, and testing
- Design the ingestion flow from other systems into S3 and Redshift
- Design and implement new partitions for Dagster and incremental loading with dbt
- Map business requirements to technical architectures
- Instruct junior team members
Stefan Corsten
Last position:
SSIS Development at Stadtsparkasse München
- Replacement of a Java application and the Oracle DB for loading the internal WerWasWo system using SSIS.
- Development of SSIS packages to load text files into the database (SQL Server)
- Development of a database project for deployment on various servers
- Creation of queries to monitor the loading runs
- Development of a PowerShell script to automate the deployment of the SSDT projects.
- Oracle, SQL Developer, Microsoft SQL Server 2022 on-premises, SQL Server Management Studio v21, Visual Studio 2022, SSIS, SSDT, PowerShell.
Discover over 15,000 top freelancers
Statistics of experts using Delta Lake
Aggregated from the professional profiles of matched freelancers.
Experience
13 years
Position duration
1.7 years
Positions per freelancer
11
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Automotive, Manufacturing
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
100%
Master's degree or higher
69%
Doctorate
27%
Certifications per freelancer
5
Most common languages
German, English, French
Speak two or more languages
100%
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 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 Germany using Delta Lake
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
What it does
Delta Lake adds reliable table semantics to data lakes. It helps teams build pipelines that support ACID writes, schema control, and time travel on top of cloud storage. That makes it a strong fit for analytics, reporting, and lakehouse setups.
Common work
- Design Delta tables for batch and streaming data
- Build ingestion and transformation pipelines in Spark
- Set schema evolution, merge logic, and compaction rules
- Support BI layers and downstream data products
- Improve data quality, auditability, and rollback options
Ecosystem
Delta Lake is often used with Apache Spark, Databricks, object storage, and orchestration tools such as Airflow or dbt. Strong specialists know how Delta files, transaction logs, and partition design affect performance and maintenance. They also understand how Delta fits into broader lakehouse architecture.
When to bring in help
Companies usually need outside expertise when pipelines become fragile, tables slow down, or teams need a clean migration from raw parquet or legacy warehouse flows. In Germany, this often comes up in analytics teams that want clearer governance and better collaboration between data and platform specialists. Freelancers can step in for design, rescue work, or focused delivery.
What good specialists do
A strong Delta Lake professional works across storage layout, Spark jobs, data contracts, and operational checks. They write clear merge logic, protect against schema drift, and know when to compact, vacuum, or rewrite tables. Good work is visible in stable runs, clean recovery paths, and simple handover notes.
Delivery focus
Delta Lake projects often include new bronze-silver-gold layers, streaming ingestion, quality checks, and table optimization. Specialists should be able to explain trade-offs between Delta Lake and plain parquet, and they should know when Databricks features matter. The best work is practical: fewer broken jobs, easier audits, and data teams that can trust the tables.
Frequently asked questions
Before you brief your next project: the most common questions about Delta Lake.
Delta Lake is used to make data lake storage more reliable for analytics and data engineering. It gives teams transactional writes, schema enforcement, and time travel on top of cloud files. That is useful when companies need trusted tables for reporting, machine learning, or streaming pipelines.
Delta Lake sits between raw file storage and a classic warehouse-style model. Compared with plain Parquet, it adds ACID transactions, versioning, and safer updates. Compared with a warehouse, it can fit more naturally into lakehouse architectures and cloud storage-based workflows.
Delta Lake started in the Databricks ecosystem, so many people still call it Databricks Delta or simply Delta. The name usually refers to the table format and transaction layer, not just one product screen. When hiring, it helps to check whether the expert has worked with open Delta tables, Databricks, or both.
A strong Delta Lake specialist usually knows Apache Spark, SQL, Python or Scala, and cloud object storage. Experience with Airflow, dbt, Databricks, and data modelling is common as well. For streaming work, knowledge of Structured Streaming and operational monitoring helps a lot.
A good Delta Lake expert can start with a clear table layout, pipeline goal, and current pain points. For rescue work, they need access to the existing Spark jobs, storage paths, and failure history. For new builds, they should also understand how the tables will be consumed by analytics or downstream products.
Yes, Delta Lake work is often done remotely, especially for design, pipeline delivery, and table tuning. For Germany-based teams, remote collaboration usually works well if the expert can communicate clearly in English and align with local stakeholders. On-site time is mainly useful for kickoff, audits, or sensitive migration phases.
Look for someone who explains trade-offs clearly and can show how Delta Lake tables stay reliable over time. Good signs are clean merge logic, sensible partitioning, clear checkpointing, and a practical approach to maintenance tasks like compaction and vacuuming. Ask how they handle schema drift, recovery, and performance issues in real projects.
Choose Delta Lake when your team needs reliable updates, rollback options, and strong pipeline control on top of cloud storage. It is often a good fit for lakehouse setups that mix batch, streaming, and BI access. If the work is mostly simple file storage with no update logic, a lighter approach may be enough.
The average hourly rate of freelancers in Germany who have used Delta Lake in their recent projects is 105 €, which corresponds to a daily rate of about 838 € based on an 8-hour working day.
Of the freelancers in Germany who have used Delta Lake in their recent projects, 100% hold at least a Bachelor's degree, 69% hold at least a Master's degree, and 27% hold a doctorate.
On average, freelancers in Germany who have used Delta Lake in their recent projects have 13 years of professional experience, with a single engagement typically lasting around 1.7 years.
The most common languages among freelancers in Germany who have used Delta Lake in their recent projects are German (100%), English (100%), and French (18%).
The most common industries among freelancers in Germany who have used Delta Lake in their recent projects are Information Technology (82%), Automotive (46%), and Manufacturing (46%).
The most common business areas among freelancers in Germany who have used Delta Lake in their recent projects are Information Technology (100%), Business Intelligence (96%), and Product Development (61%).
Main locations of FRATCH Experts, who have recently used Delta Lake
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
