Apache Spark Experts in Munich
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Meet FRATCH Experts in Munich, who have recently used Apache Spark
Tamás Eppel
Last position:
Senior Software Developer / Tech Lead at NDA (defense / OSINT)
- Designing the audit logging framework
- Implementing APIs for developers to integrate in their codebase
- Implementing ingestion pipeline, database query layer and UI for browsing the audit events
- Improving stability and reliability of the backend system
Philipp Grunert
Last position:
Data Scientist & ML Engineer at Data-Science Factory GmbH
- Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
- Implementation of automated end-to-end cloud processes
- Development of LLM and NLP models
- Creation of interactive reports
- Support for national and international large corporations as well as medium-sized companies in implementing ML projects
Christiane Neher
Last position:
Management Consultant at Christiane Neher Management Consulting
Large Insurance Company – Consultant Wiesbaden: Consulting support for the introduction of an integrated planning and performance management framework (operational, financial, customer) to enhance customer-centric transparency, decision-making quality, and steering capabilities across all lines of business within an insurance organization:
- Analysis of existing processes, reports, KPIs, and KPI calculation methodologies
- Design and introduction of new, standardized customer KPIs (gross/net), as well as key steering metrics with consistent linkage across all lines of business
- Recalculation, validation, and plausibility checks of KPIs based on existing and newly integrated data sources
- Conceptual support for the development of an integrated reporting and performance management setup
- Execution of customer insights analyses to identify patterns and anomalies within customer data clusters
Large retail company – Consultant in Karlsruhe: Advisory services for the setup and step-by-step implementation of an internationally deployable RELEX solution in the supply chain management environment:
- Advising overall and sub-project management on methodology, project setup and steering (e.g. agile approach, Jira configuration, RELEX phases, Jira Structure PPM)
- Strategic-operational consulting for the introduction of RELEX including best practices
- Support in defining overarching goals and requirements (2-year target picture)
- Guidance in scoping a relevant supply chain network segment for the project
- Development of a roadmap for iterative, incremental RELEX setup and rollout
- Assessment of project dependencies (interfaces, configurations, etc.)
- Advice on prioritized implementation of business requirements and data interfaces
- Support in test planning (data validation, system testing, UAT)
- Consulting on internationalization, change management, training, and knowledge transfer
- Stakeholder advisory and alignment activities between the client, implementation partner, and RELEX
Insurance company – Management Consultant in Munich: Analysis, consulting and support for the optimization of a large-scale business and IT transformation. Focus on strategically important programs and modernization projects in the area of Managed Services Operations and processes:
- Review of project plans and deliverables; analysis of programs and projects (e.g. cloud approach, process standardization, system integration, roadmaps)
- Identification of technical, functional and personnel risks and challenges; development of content-related measures and alternative solutions
- Proposal of quality improvements for program and modernization efforts
- Sparring partner and professional, technical, structural and organizational consulting for project and program management
Large retail group – Management Consultant & Stream Lead in Cologne: Consulting, process, project and product management for the introduction and implementation of a large strategic program in the field of advanced analytics, assortment and space management:
- Setup, test and rollout of a new space planning, automation and optimization product based on the existing cluster-based merchandising approach
- Definition and setup of new processes and transformation and change management measures for the new store-specific merchandising approach
- Collaboration with Advanced Analytics and IT (internal and external) for software implementations, automations, extensions and interfaces
- MVP approach and piloting in phases with gradual rollout (pilot with 80 stores, region with 500 stores, national level with 4000 stores)
Large retail company – Agile Coach & Change Agent in Cologne: Agile coach, OKR master and facilitator for the introduction of the OKR approach in a large strategic digitization program for retail stores:
- Coaching of the core team with topic managers and team leads
- Introduction to the OKR topic and setup of the OKR cycle
- Establishment of the OKR approach in teams and on a cross-team level
Delivery and logistics company – Management Consultant in United Kingdom: Consulting and coaching in the restructuring of the Data Analytics department:
- Analysis of current challenges
- Definition of overarching goals
- Development of a proposal for a new team structure
- Identification of required competencies, skills and responsibilities
- Advisory and alignment on communication and change management strategy
Thomas Hoefkens
Last position:
Senior MLOps, DevOps Engineer at Trianel Energy
- Build and operate an end-to-end MLOps platform on Azure ML and Kubernetes (Kubeflow) for the automated deployment, monitoring, and scaling of forecasting models (including Temporal Fusion Transformer, Informer, Autoformer).
- Implement CI/CD pipelines in Azure DevOps for the full ML lifecycle – from resource provisioning (Terraform), data transformation (Hugging Face Datasets, Pandas, PyTorch, CUDA cluster) through training and evaluation to model registry and endpoint deployment.
- Integrate MLflow for experiment tracking, model versioning, performance monitoring, and automated registration in the Azure Model Registry.
- Develop and containerize PyTorch training jobs (Azure Notebook, Jupyter Notebooks) for price and time series forecasting (PFC models) with automatic rollout via Azure ML Endpoints and REST/gRPC interfaces, Docker containerization, secured with OAuth 2.0.
- Set up monitoring and alerting mechanisms (Prometheus, MLflow Metrics), log centralization, and cost monitoring.
- Automate infrastructure provisioning and model deployment using Terraform, Helm, and Azure CLI; connect to existing market data systems and event pipelines.
- Migrate existing workloads and databases (IONOS → Azure, MongoDB) with integration into central MLOps workflows and internal networks.
- Extend the platform with LLM-based tools (LangChain, LangServe) to integrate GPT-based analysis modules into existing Spring Boot services for market anomaly detection and automated reports.
- Analyze and architect a software solution to process large volumes of data efficiently (>3000 messages/sec.) (market data store).
- Spring Boot / Java 21 container development with RabbitMQ for distributing stock market data via MongoDB (Kubernetes) with fast storage of data in Redis RMaps, deduplication, forwarding messages to Read Model queues, and building Read Models for UI display in MongoDB.
- Integration of RESTHeart to create a REST API for MongoDB.
- Build an Angular frontend to simplify data queries and master data maintenance.
- Agentic coding with remote and local LLMs (Claude Sonnet, Ollama Qwen) and MCP servers.
- Develop Python scripts for transforming and cleaning incoming stock market data (Pandas, scikit-learn).
Maik Patzwald-Feuerstein
Last position:
Freelance UX/UI Designer at Exaring AG - waipu.tv
UX/UI design for the waipu.tv web app, building custom UI libraries in Figma, as well as concepting and designing marketing landing pages and product pages.
Product Design · UI Design · Design System
Valery Khamenya
Last position:
Sr. Data Scientist & Engineer at Virtual Minds
- Development of high-performance ad distribution via auction
- Holistic (multi-campaign & multi-channel) advertisement placement optimization
- Algorithmic optimization for NP-Hard/NP-e
- Multiple Knapsack Problem with constraints
- Online estimation of parameters in stochastic environments
Tools: Python, R, Kotlin, MILP/SAT/CP Solvers, Pytorch, Pandas, Docker
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
Dino Cardiano
Last position:
Certified Coach (ICI) - Business Coach at Future Pioneers Collective
Internationally certified Master Coach, International Association of Coaching Institutes (ICI)
Future Pioneer. Future architect & bridge builder for leaders & teams - business coach for future-ready, values-based transformation in organizations.
I enable leaders, teams, and organizations to increase their future readiness in a self-determined way, master complex change, and connect value creation with purpose, responsibility, and humanity.
One-to-one coaching for leaders
Team and leadership team coaching
Conflict coaching and conflict facilitation
Process-supporting systemic coaching in transformation programs
Business coaching for transformation and change projects (innovation, culture change, New Work, intrapreneurship, agile work, leadership development), with a focus on leadership, team dynamics, conflict culture, and organizational systems.
Certified coach for non-violent communication (NVC)
Certified hypnosystemic coach
Certified conflict coach
Certified team coach
Certified career coach
Certified leadership coach
More than 6,000 hours of mentoring and coaching in the last 12 years
Manikanta Rangaswamy
Last position:
Data Engineer at Insurance client
- Design, development, and maintenance of end-to-end ETL pipelines for scalable and reliable data integration
- Support in data quality checks, testing, and migrations
- Development and maintenance of dbt models for structured, modular, and reusable data transformations
- Use of AI-driven development to improve ETL job creation and code quality.
- Development of CI/CD for automated deployment.
Hardeep Bhutter
Last position:
Sr. Data Engineer at Charles Schwab Bank
- Designed and implemented end-to-end data pipelines (batch & streaming) using Python, SQL, and Apache Spark, Databricks on AWS reducing ETL latency by 40%.
- Developed serverless event-driven ingestion pipelines using AWS Lambda and SQS, ensuring real-time data availability for downstream analytics.
- Leveraged Google Cloud Platform (GCP) services including BigQuery and Dataflow to manage cross-cloud data warehousing and analytics integration.
- Expertise in DMS (CDC, Full Load) and Airflow for scalable data pipeline automation and orchestration.
- Managed and customized data pipelines using Databricks, Airflow. Automation using Docker, Kubernetes, Terraform.
- Automated data quality checks using dbt to modularize transformations and ensure production-grade data lineage, improving reliability by 30%.
- Collaborated with compliance teams to ensure GDPR and SOC2 alignment. Mentored junior engineers and contributed to architecture refactoring for scalability.
- Created and maintained dashboards in Power BI to provide actionable insights.
Vitaliy Ryumshyn
Last position:
DevOps GitOps (temp) at Signal Iduna
- Responsible for Openshift/Kubernetes on-prem administration and developer support.
- Developed URP infrastructure automation with Python, Ansible, Kustomize and ArgoCD, Argo Workflow/Events stack.
- Wrote smoke and load tests for URP infrastructure utilizing Python, Kustomize and ApplicationSets.
- Helped to set up and deploy URP infrastructure in Google Cloud, GKE.
- Set up monitoring for URP and ArgoCD stack with Splunk Cloud.
- Performed system administration tasks across RedHat Linux, Kubernetes/Openshift, ArgoCD, GitLab, Bitbucket Enterprise, Kafka and MongoDB.
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.
Enis Spahi
Last position:
Software Developer at 50Hertz Transmission GmbH
- Participated in the gradual modernization of components into cloud-native 12-factor applications.
- Worked closely with the business operations team to eliminate manual processes and resolve several performance bottlenecks.
- Designed and implemented a CI/CD pipeline to increase developer productivity, enforce quality and security checks, and automate product delivery.
- Migrated several components into the OpenShift Kubernetes cluster.
- Built a monitoring stack from scratch with Prometheus and Grafana to monitor services running in OpenShift.
- Developed dashboards in both Grafana and Splunk for operational transparency.
- Implemented an OIDC/OAuth2-based single sign-on (SSO) solution with Keycloak to secure multiple applications.
- Technologies: Java, Spring, Quarkus, Kafka, MySQL, Cassandra, Redis, Spring Data, Hibernate, Docker, Kubernetes, OpenShift, Keycloak, OIDC, OAuth2, Helm, Prometheus, Grafana, Splunk, Spark.
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
Alyosh Agarwal
Last position:
Business Intelligence Consultant at Large Private Equity Group
- Business intelligence and KPI specification and playbook for 35 European companies.
Discover over 15,000 top freelancers
Statistics of experts using Apache Spark
Aggregated from the professional profiles of matched freelancers.
Experience
19 years (Germany: 16 years)
Position duration
2.1 years (Germany: 2.7 years)
Positions per freelancer
13 (Germany: 11)
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Banking and Finance, Automotive
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
100% (Germany: 96%)
Master's degree or higher
85% (Germany: 73%)
Doctorate
15% (Germany: 11%)
Certifications per freelancer
2 (Germany: 4)
Most common languages
English, German, Spanish
Speak two or more languages
97%
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 Apache Spark
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 Spark does
Apache Spark is a distributed processing engine for large data workloads. Teams use it to transform raw files, join large datasets, run analytics, and power streaming jobs with low latency. It is common in data platforms that need speed, scale, and flexible code.
Typical uses
- ETL and ELT pipelines
- Batch analytics and reporting
- Streaming data processing
- Feature engineering for machine learning
- Large-scale data cleansing and joins
Core stack
Strong Spark work usually includes Spark SQL, Structured Streaming, and either Scala, Python, or Java. Many projects also rely on PySpark, Delta Lake, Hive, Kafka, and object storage. Good specialists know how to shape data so jobs stay readable and stable.
When to bring in help
Companies often look for freelance Spark experts when a pipeline is slow, unreliable, or hard to maintain. The same is true when teams need help with cluster tuning, job design, migration from legacy Hadoop code, or a new streaming setup. In Munich, this often fits data-heavy teams that want short, focused support.
What strong specialists do
- Design partitioning and shuffle strategy
- Reduce bottlenecks in joins and aggregations
- Tune memory, executors, and file layout
- Write clear Spark jobs for long-term maintenance
- Debug failures across code, data, and cluster settings
Good project fit
Spark is a strong fit when data is too large for a single machine or when batch and stream logic must share the same processing model. Strong professionals can explain trade-offs between Spark, Scala-based jobs, and Python-first PySpark work. They also help teams set standards for testing, deployment, and handover so the platform stays usable after the project ends.
Frequently asked questions
Curious about Apache Spark? Here are the answers that come up again and again.
Apache Spark is used to process large data sets across many machines. Companies use it for ETL, streaming, reporting, and feature preparation when speed and scale matter. It is also common in lakehouse and analytics stacks where the same engine must handle batch and near-real-time work.
Spark is the processing engine, while PySpark is the Python API for working with it. Teams choose PySpark when they want Python-based data code, but the runtime and execution model are still Spark. A good specialist understands both the Python layer and the distributed engine underneath.
Bring in Apache Spark expertise when jobs are slow, clusters are costly, or pipelines fail in production. Freelance support also helps during migrations, cloud moves, or when a team needs a clean design for batch and streaming jobs. For Munich-based teams, short remote support is often enough, but on-site work can help during discovery or handover.
A strong Apache Spark specialist usually also knows SQL, Python or Scala, data modeling, and cloud storage patterns. Many projects also touch Kafka, Delta Lake, Hive, or Databricks-style workflows. The best experts can reason about data shape, execution plans, and operational stability, not just write code.
Spark replaced many older Hadoop MapReduce workloads because it is faster and easier to work with for many pipelines. Compared with Flink, it is often used more for batch and mixed analytics workflows, while Flink is chosen for more stream-first cases. Compared with SQL warehouses, Spark gives more control over transformations and custom logic.
Apache Spark projects need more than basic syntax knowledge once data grows or jobs must run reliably in production. For simple notebooks, a general data professional may be enough, but production pipelines need someone who understands partitions, shuffles, memory use, and failure modes. The harder the workload, the more valuable deep Spark experience becomes.
Yes, Spark work is often well suited to remote delivery because most tasks happen in code, notebooks, and cluster logs. On-site time can still help for early architecture workshops, security reviews, or working closely with local data and platform teams. For Munich companies, a mix of remote execution and a few in-person sessions is common.
A strong Apache Spark specialist can explain why a job is slow and how they would fix it before touching production code. Look for clear reasoning about partitioning, joins, skew, storage formats, and monitoring, plus examples of stable pipelines they have delivered. Good experts also write maintainable code and leave behind decisions the team can support later.
The average hourly rate of freelancers in Munich, Germany who have used Apache Spark in their recent projects is 100 €, which corresponds to a daily rate of about 801 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Apache Spark in their recent projects, 100% hold at least a Bachelor's degree, 85% hold at least a Master's degree, and 15% hold a doctorate.
On average, freelancers in Munich, Germany who have used Apache Spark in their recent projects have 19 years of professional experience, with a single engagement typically lasting around 2.1 years.
The most common languages among freelancers in Munich, Germany who have used Apache Spark in their recent projects are English (100%), German (97%), and Spanish (21%).
The most common industries among freelancers in Munich, Germany who have used Apache Spark in their recent projects are Information Technology (79%), Banking and Finance (62%), and Automotive (52%).
The most common business areas among freelancers in Munich, Germany who have used Apache Spark in their recent projects are Information Technology (93%), Product Development (79%), and Business Intelligence (69%).
Main locations of FRATCH Experts, who have recently used Apache Spark
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.
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