Delta Lake Experts in Munich
matched in minutes by AIHire experts who design reliable lakehouse architectures, implement ACID transactions and schema evolution, and connect Delta Lake with Apache Spark, Databricks and cloud storage. FRATCH matches you quickly and precisely with vetted, available freelancers.
Meet FRATCH Experts in Munich, who have recently used Delta Lake
Hachem S.
Last position:
Machine Learning & BI Lecturer at EU Business School
- Taught ML, BI, and Big Data to Master's students.
Tech: Python, Power BI, SQL, ML, SnowflakeDB, databricks, MS Fabric, MongoDB, LLM
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:
Data Scientist & 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
- Development of feature store and data synchronization between environments
- Data science: evaluation of different forecasting models, including CatBoost, Contextual Gamma and Random Forest; implementation of training pipelines using Kubeflow on Vertex AI; scaling and development of an event-driven interface using Kafka as a forecasting service; evaluation of MLFlow for model tracking and model registry
- Implementation of REST API using KeyCloak OAuth2 and OpenID (JWT)
- Monitoring: implementation of dashboards with Grafana, Prometheus, Mimir, Streamlit and Loki to monitor prediction metrics, GCP and Snowflake costs for pipelines, and system metrics
- Development of Copilot Agent Skills
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.
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)
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:
Senior AI Engineer and Technical Advisor (Freelancer) at dialpad
Serving as AI technical advisor to Dialpad's Telephony Engineering division while building AI products hands-on. Currently developing MCP-based tooling for AI text and voice agents: agent authoring, integrations and adoption of existing agents, shared authentication and API contracts, and durable workflow state. This includes text and voice tests that evaluate agent behavior, latency and task completion.
In parallel, helping Dialpad's engineers adopt QualityLayer and use Claude Code and Codex effectively. This ongoing work covers context management for large codebases, reusable plan-implement-test-review workflows connected to CI/CD, playbooks, hands-on workshops, labs and pair programming.
Technologies: AI Agent Products (Text & Voice Agents, MCP, LLMs, Python, TypeScript), Agent Tooling (Authoring, Integrations, OAuth, Durable State), Quality & Evaluation (Text & Voice Tests, Evals, Regression, Traces), Agentic Engineering (Claude Code, Codex, QualityLayer, Hooks, Prompts), Context Engineering (RAG, Vector Stores, Codebase Embeddings), Software Delivery (CI/CD Integration, Agentic Loops, PR Automation), Enablement & Coaching (Workshops, Hands-On Labs, Pair Programming)
Discover over 15,000 top freelancers
Statistics of experts using Delta Lake
Aggregated from the professional profiles of matched freelancers.
Experience
17 years
Position duration
1.7 years
Positions per freelancer
17
Top business areas
Business Intelligence, Information Technology, Product Development
Top industries
Information Technology, Professional Services, Automotive
Certification focus areas
Business Intelligence, Information Technology, Research and Development
Bachelor's degree or higher
100%
Master's degree or higher
78%
Doctorate
44%
Certifications per freelancer
4
Most common languages
German, English, Spanish
Speak two or more languages
100%
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 Munich 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 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 29 Oct 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%)
- Professional Services (70%)
- Automotive (60%)
- Media and Entertainment (60%)
- Retail (60%)
- Education (50%)
- Banking and Finance (50%)
- Healthcare (50%)
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 adds reliability to data lakes. It brings ACID transactions, schema enforcement, time travel and scalable metadata to files stored in cloud object storage. Teams use it to create lakehouse systems that support both analytics and machine learning without copying data into separate environments.
Data workloads
Delta Lake supports batch and streaming pipelines, historical analysis, feature preparation and trusted reporting. Its transaction log records table changes, enabling reproducible reads and controlled updates while data is being ingested. Common deliverables include curated tables, incremental pipelines, retention policies and recovery procedures.
Ecosystem and tooling
Delta Lake is closely associated with Apache Spark and Databricks, while integrations also cover cloud storage, SQL engines, orchestration tools and catalog services. Strong specialists understand partitioning, file sizing, compaction, schema management and performance tuning. They can also work with Python, Scala or SQL where the surrounding pipeline requires it.
- Design bronze, silver and gold table layers
- Configure streaming and batch ingestion
- Manage schema changes and table history
- Tune reads, writes and file layouts
When expertise matters
Companies usually bring in freelance Delta Lake expertise when a data lake has become difficult to trust, maintain or scale. Typical signals include duplicate pipelines, slow queries, uncontrolled schema changes or a migration from warehouse-only reporting. In Munich, specialists may also support hybrid collaboration with local data, manufacturing or financial services teams while coordinating remotely across Europe.
Delivery and governance
A capable professional connects storage design with access control, cataloging, lineage and operational monitoring. They define table ownership, quality checks, deployment workflows and rollback practices rather than treating the transaction log as a standalone feature. They also assess whether Delta Lake fits the existing cloud, compliance and query environment.
Choosing a specialist
Look for evidence of production lakehouse work, not only familiarity with Databricks terminology. Ask how the person handled late-arriving data, concurrent writes, schema evolution, small files and failed streaming jobs. Strong specialists explain trade-offs against Apache Iceberg or Apache Hudi, document design decisions and communicate clearly in English; German can help for close on-site work in Munich.
Frequently asked questions
The facts hiring teams ask for most often when it comes to Delta Lake.
Delta Lake is used to make cloud-based data lakes more reliable and easier to operate. It supports transactional updates, streaming ingestion, historical table versions, schema controls and analytics or machine learning workloads on shared data.
Delta Lake is often weighed against Apache Iceberg and Apache Hudi because all three add table-management features to data lake storage. The practical choice depends on the existing engines, catalog services, cloud environment and team experience, not on storage format alone.
A strong Delta Lake specialist usually understands Apache Spark, Databricks, cloud object storage, SQL and pipeline orchestration. Experience with streaming, data quality, access control, cataloging and observability is also valuable for production work.
The right level depends on the project scope. A focused table migration may need someone comfortable with Spark and transaction logs, while a lakehouse redesign calls for proven judgment around architecture, governance, performance and operational recovery.
Delta Lake works well for streaming when ingestion, checkpointing, late data and failure recovery are designed carefully. A specialist should also explain how streaming writes interact with batch reads, schema changes and downstream table refreshes.
Delta Lake projects are often suitable for remote collaboration because most work happens in cloud environments, repositories and shared documentation. On-site sessions in Munich can still help with stakeholder workshops, data ownership decisions and coordination with local teams; English is common, while German may be useful in some organizations.
Ask for a clear design covering table layout, partitioning, schema evolution, retention, testing and monitoring. A reliable specialist can discuss trade-offs, demonstrate safe recovery from failed jobs and show how the transaction history supports auditability without promising that it solves every governance issue.
Common mistakes include creating too many small files, choosing poor partition columns, allowing uncontrolled schema changes and skipping data-quality checks. Teams can also overlook catalog integration, access policies and retention settings, which makes a technically correct implementation difficult to operate.
The average hourly rate of freelancers in Munich, Germany who have used Delta Lake in their recent projects is 101 €, which corresponds to a daily rate of about 808 € 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, 78% hold at least a Master's degree, and 44% hold a doctorate.
On average, freelancers in Munich, Germany who have used Delta Lake in their recent projects have 17 years of professional experience, with a single engagement typically lasting around 1.7 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 (20%).
The most common industries among freelancers in Munich, Germany who have used Delta Lake in their recent projects are Information Technology (100%), Professional Services (70%), and Automotive (60%).
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 (90%).
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
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Philipp Thomaschewski
FRATCH CEO
