Delta Lake Experts in Munich
matched in minutes from over 15,000 CVsHire experts who design dependable lakehouse architectures, implement Delta tables and optimize Spark data pipelines. FRATCH connects you with precise matches from vetted, available freelance professionals quickly.
Meet FRATCH Experts in Munich, who have recently used Delta Lake
Mirza K.
Last position:
Agentic Automation and a RAG system
- This project involved extraction of intelligence data to support report writing for a company that provides geopolitical, global, commercial intelligence. The data have been gathered from a number of resources (interview transcripts, online data, internal documents), and then a knowledge base has been build from it. This was the basis of a complex RAG system, that was evaluated against a golden dataset. Agents have been used to find out the contradicting intelligence, the statements supporting each other, and to store back the generated knowledge.
Used: Python, RAG, LangGraph, LangChain, deepeval, MCP
Ajay Kumar D.
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.
Serge K.
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
Christian S.
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 N.
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
Stephan S.
Last position:
Senior Data/ML Consultant & Technical Lead at Jolin.io
Role: Software Engineer & Applied Mathematician (Mathematical optimization for scheduling; duration: 1 months; team setting: Team of 2, remote; technologies: JuMP, Julia, Pluto, Svelte, JavaScript, TypeScript, JetBrains Space, Terraform, Nomad)
Role: Software & Cloud & Web Engineer (Building scalable data science compute cluster from scratch; duration: 11 months; team setting: Team of 1, on-site; technologies: Terraform, Kubernetes, k8s ingress, k8s services, k8s RBAC, k8s networking, k3s, etcd, S3, DNS, certificates, Julia, Pluto, JavaScript, Tailwind, Astro, npm, Parcel, Preact, MUI, JWT, AWS SQS, AWS RDS, Python, GitLab, GitHub)
Role: AI & Web Engineer (Custom ChatGPT service; duration: 1 months; team setting: Team of 2, remote; technologies: Python, Poetry, LangChain, Tailwind, ChatGPT API, Flask, FastAPI)
Role: Architect & Data Engineer (Central datalake setup and ingestion; duration: 9 months; team setting: Team of 5, remote; technologies: Infrastructure-as-code, AWS CDK, Python, Boto3, PySpark, AWS Glue, IAM, S3, ECS, Fargate, Lambda, Apache Hudi, DeltaLake, Databricks, GitHub, Jira, Miro)
Role: Software Engineer (PoC Julia migration of scikit-decide; duration: 1 months; team setting: Team of 2, remote; technologies: Python, Julia, GitHub)
Stefan C.
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.
Himanshu N.
Last position:
Principal (Data Scientist/Data Engineer/Gen AI Engineer) at Marktguru Deutschland GmbH
Architected an agentic, real-time offer orchestration engine where specialized agents (retrieval, pricing/optimization, and policy/guardrails) coordinate to personalise promotions across customer touchpoints using RAG with FAISS over Delta Lake and low-latency Databricks Model Serving. Collaborated with product managers and commercial stakeholders to shape the roadmap and evaluate emerging agent patterns for production.
Designed an agent-based data quality service that orchestrates schema detection, entity normalization, and validator/exception-handling agents to clean multi-retailer SKU feeds at scale. Wrapped model calls in PySpark UDFs for distributed inference, automated via Databricks Workflows and CI/CD.
Developed a multimodal, agentic extraction pipeline where vision, parsing, and compliance agents collaborate to derive brand, packaging, and volume from scanned images using Claude 3 Sonnet with Swin Transformer encoders. Orchestrated via Azure Event Hub with outputs persisted to Delta Lake.
Implemented a GS1 taxonomy classification service built around cooperating agents for inference, drift monitoring, and auto-retraining governance using Falcon 180B (LoRA-tuned) with a batch pipeline on Databricks.
Created a hybrid agent workflow where a retrieval agent surfaces candidate matches via embeddings and a reasoning/verification agent (Mixtral 8x7B) adjudicates receipt-to-SKU alignment, integrated into a streaming Databricks pipeline.
Built a multimodal attribute inference pipeline structured as cooperating vision-language, rules/consistency, and compliance agents to fill NutriScore, nutrition fields, and packaging types from names and images using LLaMA 3-8B with CLIP embeddings.
Developed a GenAI-powered orchestration system that ingests recipes from multiple websites, parses ingredients through structured extraction agents, and dynamically links them to real-time retailer offers via tagging, semantic reasoning, and business-rule agents.
Max R.
Last position:
Cloud (AWS) | AI | DevOps | Data at Boehringer Ingelheim
- Architected and implemented an enterprise-grade AI Agent Platform leveraging Retrieval Augmented Generation (RAG) architecture to enhance clinical data insights.
- Established robust CI/CD pipelines for LLM applications using CDK and Jenkins, significantly reducing deployment times.
- Implemented comprehensive observability solutions that increased agent reliability across pharmaceutical environments.
- Designed scalable AI workflows with advanced orchestration that optimized context handling for enterprise data sources.
- Technologies: AI Agents (LangChain, LangGraph, Bedrock, Smolagents, Streamlit); LLM Operations (Tracing, Testing, Evaluation, LangSmith, LangFuse); Infrastructure-As-Code (AWS CDK, Terraform, Typescript, Jenkins); Vectors, Embeddings, RAG (OpenSearch, pgvector, PDF Extraction)
Discover over 15,000 top freelancers
Statistics of experts using Delta Lake
Aggregated from the professional profiles of matched freelancers.
Experience
18 years
Position duration
1.8 years
Positions per freelancer
18
Top business areas
Business Intelligence, Information Technology, Product Development
Top industries
Information Technology, Media and Entertainment, Professional Services
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
100%
Master's degree or higher
75%
Doctorate
50%
Certifications per freelancer
4
Most common languages
German, English, Spanish
Speak two or more languages
100%
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 Munich 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 Munich 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 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Delta Lake 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%)
- Media and Entertainment (67%)
- Professional Services (67%)
- Retail (67%)
- Automotive (56%)
- Banking and Finance (56%)
- Manufacturing (56%)
- Education (44%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Lakehouse foundation
Delta Lake is an open storage layer that brings transactions, schema enforcement and reliable updates to data lakes. It works with cloud object storage and supports lakehouse architectures that combine flexible data storage with warehouse-style controls. Teams use it for analytics, machine learning and operational reporting.
Core capabilities
Delta tables store data in Parquet files with a transaction log that records changes. This enables consistent reads and writes, time travel, table history and controlled schema evolution. Specialists also use partitioning, compaction and data retention processes to keep tables efficient and manageable.
Ecosystem and tooling
Delta Lake is closely associated with Apache Spark and Databricks, while integrations also exist across major cloud data services and processing engines. Strong professionals commonly work with Python, SQL, Scala, Spark Structured Streaming and orchestration tools. They connect Delta tables to catalogs, notebooks, dashboards and machine learning workflows.
Typical projects
- Replace fragile file-based pipelines with transactional Delta tables
- Create batch and streaming ingestion for customer, product or event data
- Design medallion architectures with governed bronze, silver and gold layers
- Recover historical table versions and manage schema changes safely
- Tune storage layout, queries and maintenance jobs for production workloads
When to bring in expertise
Companies often need freelance expertise when a data lake has inconsistent schemas, duplicate records, slow queries or unclear ownership. A specialist can assess the existing storage and pipelines, define table standards, migrate critical datasets and document operational procedures. In Munich, this can support retail, manufacturing, mobility, finance and media teams working with local stakeholders or distributed delivery groups.
What strong specialists deliver
Good Delta Lake professionals understand more than table syntax. They reason about data contracts, partition strategy, streaming guarantees, access controls, catalog design, observability and recovery. They define practical tests for correctness and performance, explain trade-offs clearly, and leave behind maintainable pipelines that internal teams can operate after the engagement.
Frequently asked questions
The facts hiring teams ask for most often when it comes to Delta Lake.
Delta Lake is used to make cloud data lakes more reliable for analytics, reporting and machine learning. It adds transactions, schema controls, table history and consistent updates to files stored in object storage.
Delta Lake and Apache Iceberg both add table management and transactional behavior to data lakes, while Apache Hudi focuses strongly on incremental processing and record-level updates. The right choice depends on the processing engines, catalog services, cloud environment and operational standards already used by the company.
A strong Delta Lake specialist usually works with Apache Spark, SQL, Python or Scala, cloud object storage and workflow orchestration. Experience with data catalogs, streaming, access management, testing and observability is also valuable for production work.
The required depth depends on the scope. A table migration may need focused knowledge of schemas, transactions and maintenance, while a lakehouse redesign calls for broader experience with Spark, governance, security, cost control and recovery procedures.
Yes. Delta Lake work is well suited to remote collaboration because architecture reviews, code changes, pipeline testing and documentation can be handled online. On-site sessions in Munich can still help when the specialist must align closely with data owners, security teams or business stakeholders.
Ask for concrete examples involving schema evolution, streaming or batch ingestion, table optimization and failed-job recovery. A capable Delta Lake freelancer should explain design decisions in terms of data volume, access patterns, reliability and the team’s ability to operate the result.
Delta Lake originated in the Databricks ecosystem, but the open-source project can be used with compatible engines and cloud storage outside Databricks. A freelancer should confirm connector support, feature compatibility and catalog behavior before recommending a non-Databricks setup.
A quality Delta Lake implementation has clear table ownership, documented schemas, controlled retention, tested pipelines and monitoring for freshness and failures. It also uses sensible partitioning and compaction rather than treating storage maintenance as an afterthought.
The average hourly rate of freelancers in Munich, Germany who have used Delta Lake in their recent projects is 109 €, which corresponds to a daily rate of about 869 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Delta Lake in their recent projects, 100% hold at least a Bachelor's degree, 75% hold at least a Master's degree, and 50% hold a doctorate.
On average, freelancers in Munich, Germany who have used Delta Lake in their recent projects have 18 years of professional experience, with a single engagement typically lasting around 1.8 years.
The most common languages among freelancers in Munich, Germany who have used Delta Lake in their recent projects are German (100%), English (100%), and Spanish (22%).
The most common industries among freelancers in Munich, Germany who have used Delta Lake in their recent projects are Information Technology (100%), Media and Entertainment (67%), and Professional Services (67%).
The most common business areas among freelancers in Munich, Germany who have used Delta Lake in their recent projects are Business Intelligence (100%), Information Technology (100%), and Product Development (89%).
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.
Countries:
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.
Would you rather directly get in touch?
We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
