AWS Glue Experts in Munich
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Meet FRATCH Experts in Munich, who have recently used AWS Glue
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.
Alexandre Savio
Last position:
Cloud Engineer at Dectris AG
- Build a scalable multi-region backend service in AWS to serve remote desktop virtual machines for scientific analysis
- Stack: AWS, GitHub, Terraform, Python, Rust
- Built and defined the core infrastructure of the backend system
- Defined and coded the virtual machines provisioning supporting Ubuntu and Rocky Linux desktop setups
- Programmed the API service running in ECS to manage virtual machines and build custom Docker images for users
Stephan Sahm
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)
Alexandru Gunescu
Last position:
Head of Cloud Infrastructure at BP
- Migrated the Electric Vehicle Charging SaaS App of the EV Division from on-premises and Azure to AWS Cloud, resulting in a hybrid multi-cloud multi-tenant solution
- Developed a streaming data pipeline using AWS MSK for Apache Kafka and implemented an event-driven architecture to ingest and process near real-time data from OCPI-protocol IoT devices
- Implemented multi-tenant strategies including database schema isolation, bridge model for resource sharing, and tenant-based RBAC controls
- Provisioned Kubernetes clusters on AWS EKS with namespaces and RBAC for tenant isolation
- Led migration from on-premises and Azure to AWS using AWS DataSync, Snowball, and Database Migration Service
- Orchestrated collaboration across 5+ systems, vendors, service providers, and on-site teams
- Supported development and maintenance of IT strategy aligned with business requirements
- Managed €40 million infrastructure budget with AWS & Azure cost optimization, achieving 15% savings
- Led 50+ developers to implement advanced database procedures, increasing productivity by 20%
- Spearheaded multi-cloud, multi-tenant infrastructure migration for 30% faster processing times
- Negotiated vendor pricing to reduce payroll/benefits administration costs by 20%
- Developed a two-year infrastructure technology roadmap yielding 25% cost savings
- Tech stack: Kubernetes on AWS EKS, Docker, Kafka/AWS MSK, Terraform, AWS CDK, TypeScript, React, NextJS, Node.js, NestJS, Python, Aurora Serverless, RDS (MySQL, SQL Server), GitHub Actions, Azure DevOps, ArgoCD, AWS Lambda, API Gateway, AWS Security Hub, AWS Database Migration Service, AWS DataSync, AWS Organizations, AWS Control Tower, Odoo, Microsoft Navision, MS Dynamics
Maziyar Khorrami
Last position:
Data Engineer at MSD Germany
- Lead Architect to design and implement the data lake and ETL Pipeline using AWS Stack
- Performance Optimization of Data Ingestion of ETL Pipeline
- Development of Data Validation using Great Expectations
- Leading of the data migration for two sources exchanges
- Data Modeling in AWS Redshift
MLOps
- Model inference implementation by mlflow and AWS SageMaker
- Feature Engineering for the running ML Models ( Recommender Engineer, Clustering )
- Implementatino of Model Registry and artifactory using mlflow
- Historization an Profiling of the Input Data Using AWS Glue Crawler and AWS Data Catalog
- Feature importance using mlflow
Tech. Stack: Python 3, AWS Glue, AWS Step Fucntion, AWS Lambda, AWS EventBridge, AWS IAM Role, AWS SageMaker, AWS EC2, AWS Glue Crawler, AWS CloudWatch, MLFlow, ETL, Data lake, GitHub Action, Terraform, Jenkins, Ansible playbooks (Infrastructure as Code), CI/CD, GitLab, SQL, PySparkSCRUM, Agile, Jira, BigData, VSCode, DBeaver, MSSQL, MySQL, grafana, Docker, Linux, Bash, MapReduce, Data Modeling (ORM), Pandas, YAML, SQL-Alchemy
Max Wolke
Last position:
Fractional growth leader at wolke.studio
wolke.studio is my new outfit —> your growth plugin, fit for the AI era. I work with seed to series b startups to build scalable growth engines, and scaleups to orchestrate complex growth functions that span marketing and sales.
Thorsten Gutermuth
Last position:
Head of Real Estate Products | Technical Program Lead
- Program lead for a multi-year strategic partnership (~€9M device volume), reporting to the CTO (later CEO) and acting as primary executive interface to partner leadership.
- Owned cross-system delivery across firmware, hardware, cloud backend, manufacturing and partner engineering teams for two IoT products; improved system stability and observability to support reliable large-scale field operations (125k+ devices).
- Negotiated program roadmap and scope with partner leadership, aligning delivery commitments across hardware, firmware and cloud.
- Stabilized a strained executive partnership by restoring delivery reliability and establishing clear governance and scope boundaries.
- Delivered telemetry analysis system (Python, AWS) required by partner contract to detect malfunctioning heating systems and operational issues across deployed devices.
Jiri Sostok
Last position:
Quality Manager/Test Management at Noriba GmbH
- Creation of test concepts
- Development of test processes
- Coordination of test case development: stress tests, functional tests, performance tests, high data rate tests, integration tests, etc.
- Hardware testing: FPGA, RF
- Test automation and regression testing
- Ensuring 24/7 operation of the test system
- Analysis & reporting
- Regular coordination of the test team, meetings with other stakeholders
- Communication and coordination with stakeholders and project managers
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 AWS Glue
Aggregated from the professional profiles of matched freelancers.
Experience
18 years (Germany: 15 years)
Position duration
2.8 years (Germany: 2.1 years)
Positions per freelancer
9
Top business areas
Information Technology, Product Development, Project Management
Top industries
Information Technology, Banking and Finance, Retail
Certification focus areas
Information Technology, Business Intelligence, Operations
Bachelor's degree or higher
89% (Germany: 93%)
Master's degree or higher
67% (Germany: 64%)
Doctorate
11% (Germany: 18%)
Certifications per freelancer
3 (Germany: 4)
Most common languages
English, German, Spanish
Speak two or more languages
90% (Germany: 94%)
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 AWS Glue
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
ETL on AWS
AWS Glue is a serverless service for building data integration workflows on AWS. It is used to extract, transform, and load data from databases, files, and streams into warehouses, lakes, and analytics tools. Strong professionals keep pipelines readable, reliable, and easy to operate.
Common deliverables
- Glue ETL jobs in Python or Scala
- Crawlers and the Glue Data Catalog
- Job scheduling and workflow orchestration
- Schema handling and partitioned data layouts
- Monitoring, retries, and failure handling
Tooling and ecosystem
AWS Glue often sits next to Amazon S3, Athena, Redshift, Lake Formation, IAM, and CloudWatch. It also connects with Spark-based transformations and data sources across AWS and external systems. Good specialists know how these pieces fit together without creating brittle pipelines.
When to bring in help
Companies usually need freelance expertise when a data platform is growing, the catalog is inconsistent, or ETL jobs are slow and hard to support. AWS Glue specialists also help during migrations from legacy batch scripts or managed ETL tools. In Munich, this often matters for teams working across manufacturing, mobility, finance, and SaaS data stacks.
What strong experts do
Strong professionals design jobs that are easy to test and maintain. They pay attention to schema drift, partition strategy, access control, and cost-aware runtime choices. They also document the flow so other experts can extend it safely.
Working style
AWS Glue work is often remote, but on-site sessions help when data ownership, security, or legacy source systems need close coordination. For Munich-based teams, English is common in technical work, while German can help with stakeholders and internal reviews. The best results come from clear source lists, sample data, and well-defined output tables.
Frequently asked questions
Questions about AWS Glue? Start with the answers below.
AWS Glue is used to move and transform data for analytics, reporting, and lakehouse pipelines. Teams use it to read from source systems, clean and reshape records, and write results into Amazon S3, Redshift, or other targets. It is a common fit when the work needs to stay serverless and easy to schedule.
AWS Glue builds on Spark, but it adds managed crawling, cataloging, scheduling, and AWS integration around the compute layer. Compared with custom scripts, it is easier to standardize and operate. Compared with a full Spark platform, it can be quicker to adopt when the workflow is mostly AWS-native.
A strong AWS Glue specialist usually knows Python or Scala, Spark concepts, IAM, Amazon S3, data modeling, and logging. They should also understand schema evolution, partition design, and how to troubleshoot jobs that fail on bad source data. Experience with the Glue Data Catalog and Athena is often important too.
Companies usually bring in AWS Glue expertise when they need to speed up a pipeline, fix recurring failures, or connect more systems than the internal team can handle alone. It is also useful during cloud migration or when existing jobs are hard to maintain. A freelancer can help stabilize the design before the workload grows further.
Yes, AWS Glue work is often well suited to remote collaboration because most tasks happen in code, configuration, and AWS consoles. On-site time can still help when teams need fast decisions on source systems, security boundaries, or business definitions. For Munich teams, a mix of remote delivery and in-person workshops is common.
Ask which source systems they have integrated, how they handle schema changes, and how they monitor failures in AWS Glue. You should also ask how they design partitions, whether they use the Data Catalog well, and how they document the pipeline for handover. Clear answers here usually separate hands-on specialists from general AWS knowledge.
No, AWS Glue is a strong fit for AWS-centered batch and orchestration work, but not every case needs it. Very simple transformations may be easier with lighter tools, while highly custom data engineering may call for a different stack. The best choice depends on source systems, data volume, and how much managed AWS integration you want.
Good AWS Glue work is easy to run, easy to trace, and easy to change. Look for clear naming, sensible error handling, stable catalog usage, and output that matches the downstream query pattern. If the freelancer also explains trade-offs and leaves clean documentation, that is a strong sign of quality.
The average hourly rate of freelancers in Munich, Germany who have used AWS Glue in their recent projects is 120 €, which corresponds to a daily rate of about 960 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used AWS Glue in their recent projects, 89% hold at least a Bachelor's degree, 67% hold at least a Master's degree, and 11% hold a doctorate.
On average, freelancers in Munich, Germany who have used AWS Glue in their recent projects have 18 years of professional experience, with a single engagement typically lasting around 2.8 years.
The most common languages among freelancers in Munich, Germany who have used AWS Glue in their recent projects are English (100%), German (90%), and Spanish (20%).
The most common industries among freelancers in Munich, Germany who have used AWS Glue in their recent projects are Information Technology (100%), Banking and Finance (60%), and Retail (50%).
The most common business areas among freelancers in Munich, Germany who have used AWS Glue in their recent projects are Information Technology (90%), Product Development (80%), and Project Management (80%).
Main locations of FRATCH Experts, who have recently used AWS Glue
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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Get in touch with the FRATCH team and we will get back to you within 4 hours.
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Philipp Thomaschewski
FRATCH CEO
