Delta Lake Experts in Munich
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Meet FRATCH Experts in Munich, who have recently used Delta Lake
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.
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
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
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.
Himanshu Negi
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 Ritter
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
19 years
Position duration
1.8 years
Positions per freelancer
19
Top business areas
Business Intelligence, Information Technology, Product Development
Top industries
Information Technology, Retail, Manufacturing
Certification focus areas
Business Intelligence, Information Technology, Operations
Bachelor's degree or higher
100%
Master's degree or higher
67%
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 30 Aug 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What it is
Delta Lake adds ACID transactions, schema enforcement, and time travel to data lakes. It is used to keep large datasets consistent while teams query them with Spark and SQL. Many search for it as Delta or Databricks Delta Lake.
Typical work
- Build reliable ETL and ELT pipelines
- Set up streaming ingestion and incremental processing
- Create bronze, silver, and gold table layers
- Improve data quality with schema checks and constraints
- Support analytics and machine learning data feeds
Core skills
Strong specialists know Spark, Parquet, and object storage well. They also understand partitioning, file compaction, vacuuming, and table design. In Munich, this often matters for teams in automotive, manufacturing, finance, and software that run mixed cloud and lakehouse workloads.
Ecosystem fit
Delta Lake sits in the Apache Spark and lakehouse ecosystem. It works with Databricks, cloud storage, SQL engines, orchestration tools, and notebooks. Good professionals know how to keep tables readable, performant, and ready for downstream reporting.
When to bring in help
Bring in freelance expertise when table layouts are messy, pipelines are slow, or streaming jobs need safer updates. It also helps when a team is moving from raw data lake storage to Delta Lake or standardizing patterns across projects. Remote work is common, but Munich-based collaboration can help with workshops and handover sessions.
What strong specialists deliver
A strong Delta Lake specialist writes clean, maintainable table logic and thinks in data contracts, not just jobs. They can explain trade-offs between append, merge, and overwrite patterns, then document the setup for other experts. They leave a lakehouse that is easier to operate and easier to trust.
Frequently asked questions
The facts hiring teams ask for most often when it comes to Delta Lake.
Delta Lake is used to make data lakes more reliable for analytics and data engineering work. It adds transactional safety, schema control, and time travel so teams can work with changing data more confidently. Companies use it for batch pipelines, streaming ingestion, and curated lakehouse tables.
A plain data lake usually stores files without strong table guarantees. Delta Lake adds ACID transactions, versioned tables, and safer updates, which reduces broken reads and partial writes. That makes it easier to build dependable analytics on top of object storage.
No. Delta Lake is closely associated with Databricks, but the format and table layer are used in wider Spark-based and lakehouse setups too. In practice, the right specialist should know how it fits with cloud storage, Spark, and the tools your team already uses.
A strong Delta Lake specialist usually knows Apache Spark, SQL, Parquet, and cloud object storage. Knowledge of streaming, orchestration, partitioning, and file maintenance is also important. For many projects, data modeling and performance tuning matter as much as the table format itself.
It depends on the scope, but Delta Lake work is rarely just a quick setup task. Simple table creation is one thing; safe merges, streaming upserts, and production operations need deeper experience. If the project affects reporting or core data flows, choose someone who has handled real operational data.
If your tables are inconsistent, your pipelines are hard to rerun, or your team keeps patching failed writes, Delta Lake expertise can help. It is also useful when duplicate records, schema drift, or slow merges are slowing delivery. These are common signs that table design and data operations need review.
Yes, much of Delta Lake work can be done remotely because it centers on code, table design, and data pipeline reviews. Munich-based workshops can still help when teams want close coordination with local stakeholders or on-site whiteboarding. Many projects use a mix of remote delivery and in-person sessions.
Look for someone who can explain table design, failure handling, and maintenance clearly. A good Delta Lake professional should talk about merge patterns, schema evolution, compaction, and how to keep pipelines predictable over time. Clear documentation and practical trade-offs are usually better signs than vague tool lists.
The average hourly rate of freelancers in Munich, Germany who have used Delta Lake in their recent projects is 105 €, which corresponds to a daily rate of about 841 € 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, 67% 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 19 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 (14%).
The most common industries among freelancers in Munich, Germany who have used Delta Lake in their recent projects are Information Technology (100%), Retail (86%), and Manufacturing (71%).
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 (86%).
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
