Amazon Redshift Experts in Munich
, matched in minutes from over 15,000 CVsHire experts who design Redshift data warehouses, optimize SQL and distribution keys, and connect AWS analytics services such as S3, Glue and Spectrum. FRATCH matches you quickly with vetted, available freelancers who fit your technical needs.
Meet FRATCH Experts in Munich, who have recently used Amazon Redshift
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
Deepa K.
Last position:
Data Analyst – BI Lead Engineer at Novartis
- Leading enterprise BI transformation across Power BI & Microsoft Fabric, delivering scalable data models, automated reporting, and high-performance analytics solutions for commercial and operational leadership.
- Building and optimizing Power BI Dataflows, Fabric Lakehouse datasets, semantic models, and automated reporting pipelines to improve data scalability, governance, and reporting performance.
- Driving dashboard modernization and KPI governance by translating complex business requirements into executive-level insights, interactive visualizations, and decision-ready analytics.
- Designing end-to-end Microsoft Fabric architectures integrating data ingestion, transformation, virtualization, and enterprise reporting across cross-functional business domains with SAP BW to Qlik to Power BI migration.
- Delivering AI-enabled reporting capabilities, threshold-based alerting, and automation frameworks within the Power BI ecosystem to accelerate business decision-making.
- Partnering with commercial leadership, analytics teams, and IT stakeholders to standardize KPIs, optimize BI strategy, and deliver scalable, business-critical reporting solutions.
- Recognized for combining strong stakeholder leadership, technical architecture expertise, and business-driven analytics to deliver impactful enterprise BI transformation initiatives.
Alexandru G.
Last position:
Principal Cloud DevOps Architect at BP
In my role as Senior Cloud DevOps Architect for BP, an oil and gas company, I had the mission to migrate the Electric Vehicle Charging platform of the EV Division from on-premises and Azure to AWS cloud, resulting in a hybrid multi-cloud, multi-tenant SaaS solution.
Deployment with Kubernetes for the application layer meant provisioning Kubernetes clusters managed by EKS and AKS, with a focus on integrating them into a multi-tenant environment. This integration was achieved by using Kubernetes namespaces and access controls to ensure data isolation and privacy enforcement.
In the database layer, we chose an RDS instance with PostgreSQL to support the backend infrastructure of our applications. Tenants shared the same RDS instance, but each had a dedicated schema.
To ingest near real-time data from physical charge points (CPOs), as IoT devices, via the OCPI protocol, we ran into significant delays with batch processing. As a result, we built a real-time streaming data pipeline using Apache Kafka, while prioritizing an event-driven architecture.
Led collaboration across multiple internal teams, external vendors, cloud providers, and on-site partners to integrate over five systems into a unified solution.
Achievements:
- Successfully designed and implemented hybrid multi-cloud solutions, integrating multiple cloud platforms (AWS, Azure) with on-premises infrastructure, using Site-to-Site VPNs, Firewalls, and Load Balancing.
- Led the migration of on-premises infrastructure to multi-cloud, multi-tenant infrastructure, resulting in 30% faster processing times.
- Migrated workloads from VMware and Hyper-V environments to cloud-based VMs, leveraging cloud-native services to optimize performance, cost efficiency, and scalability.
- Designed a multi-tenant Kubernetes platform leveraging the Kubernetes ecosystem, using Karpenter for dynamic EC2 node provisioning, KEDA for event-driven pod autoscaling (e.g., Kafka message lag), and Rancher for centralized monitoring of multiple clusters (EKS, AKS, or on-prem K8s), replacing Microsoft-centric Azure Arc management service.
- Designed and implemented Python-based FastAPI microservices as part of the EV core-backend on AWS EKS application layer, powering data ingestion and customer analytics pipelines.
- Developed asynchronous, event-driven APIs (Python-FastAPI) for real-time integration with CPOs, supporting OCPI 2.3 and OICP protocols.
- Designed and implemented a secure, production-grade Azure Databricks platform using Terraform, ensuring scalability and cost efficiency.
- Migrated on-premises ERP to a hybrid Dynamics 365 architecture with ERP hosted locally and CRM running in Azure, integrated via Azure Arc.
- Automated CI/CD pipelines for Databricks notebooks and jobs using GitHub Actions & Databricks CLI, reducing deployment time. Reduced infrastructure provisioning time by 70% by automating cloud resource deployment with GitOps.
- Ensured compliance with internal audit and data governance standards (GDPR) through OAuth2/OIDC-based authentication and fine-grained role-based access controls.
- Developed a Zero Trust security model, enforcing least-privilege access and microsegmentation, enhancing security posture and compliance with GDPR and NIST.
- Built interactive analytics dashboards in Amazon QuickSight, integrating data from S3 and Redshift to deliver real-time business insights and visualizations with embedded access for multi-tenant users.
- Led cloud security assessments and full-lifecycle cybersecurity integration during M&A, covering AWS, Azure, IAM (Entra ID), and data protection, while aligning security posture with NIST, ISO 27001, and GDPR across hybrid and cloud-native environments.
- Reduced cloud costs by 64% for a client's dev environment by implementing automated start/stop schedules for EC2 and RDS instances via AWS CDK with EventBridge Scheduler or AWS Systems Manager.
Tech stack:
- Infrastructure as Code: Terraform, AWS CDK, Ansible.
- Containers: Kubernetes on EKS, AKS, Docker.
- Streaming Data Processing: Kafka to Confluent Cloud, after AWS MSK.
- Frontend: TypeScript, React, NextJS, Hooks, Styled Components.
- Backend: Python with FastAPI, also Node.js with NestJS.
- Database: Aurora on PostgreSQL with TypeORM, RDS on SQL Server, Azure Databricks full setup and administration, ETL Pipelines.
- CI/CD and GitOps: GitHub Actions, Azure DevOps, ArgoCD.
- Monitoring and Observability: Prometheus and Grafana.
- Virtualization: Hyper-V, VMware Cloud on AWS, Azure Migrate.
- ERP Systems: Odoo, Microsoft Dynamics 365 Business Central on Azure, integrated with Azure Arc.
- Networking: Site-to-Site VPNs, AWS Direct Connect, Azure ExpressRoute, Firewalls (AWS Network Firewall, Azure Firewall).
- Security: IAM, NIST Framework, Zero Trust Security, AWS WAF, AWS Shield, GuardDuty.
Suyash S.
Last position:
Data Analyst - Reporting & Analytics at SIXT SE
- Developed & maintained customer analytical reporting solutions to identify revenue trends, performance drivers, risks & optimization opportunities to ensure data driven decision making across Sales, Finance, Product, Data Engineering & Controlling.
- Defined & analyzed customer trends & performance metrics to identify root causes behind variances, anomalies & emerging risks across business domains to deliver actionable recommendations.
- Developed & owned analytical data models & reporting layers to ensure scalability, performance & analytical robustness to support executive & operational reporting across business domains.
- Planned, tracked & executed projects by ensuring adherence to timelines, data accuracy, consistency, deliverables, reliability & data quality standards through rigorous validation & reconciliation processes.
- Raised the analytical maturity by formalizing analytical workflows, documenting data processes & standard operating procedures (SOPs) & conducting training sessions to drive adoption of self-service analytics & embed a data driven culture across operational and business teams.
- Took ownership of the end-to-end lifecycle roadmap from requirement gathering, collection, transformation, developing robust business logics to data storytelling & stakeholder delivery.
- Converted complexity into structured clarity by translating requirements & business processes into analytical recommendations to ensure alignment between non-technical & technical stakeholders.
- Conducted advanced SQL based analysis of complex business datasets to uncover trends, correlations & performance improvement opportunities.
- Drove process automation & efficiency improvements by leveraging Python, SQL optimization & AI assisted tools to reduce processing time & increase reliability across analytical & operational workflows.
- Standardized KPI definitions & reporting logic to ensure consistency & trust across reporting solutions.
- Developed process monitoring dashboards & analyses to identify inefficiencies, bottlenecks & compliance deviations across end-to-end business processes to derive actionable recommendations for process improvement & automation.
Eli R.
Last position:
Technical co-founder at AskTheLaws
- Create an AI legal assistant with modern ML capabilities.
- Implement RAG architecture, with data pipelines for legal data search.
- Use AWS Bedrock for LLM and embedding models and LangChain/LangGraph
- Python with FastApi for backend and React for frontend
Hardeep B.
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.
Paul W.
Last position:
Agentic AI Solution Architect at Solvd GmbH
As the Solution Architect for Agentic AI in auto claims processing, I led global customer delivery implementations, encompassing solution design and detailing, multi-tenancy, process flows, integration with third-party solutions, and localization requirements.
- Architectural Analysis: Conducted in-depth analysis of business requirements, managing requirements and creating detailed specifications.
- Service Definition: Developed comprehensive technical definitions for services and integration contracts.
- AI Process Management: Automated AI process management, focusing on analysis, optimization, and continuous improvement.
- Requirements Gathering: Facilitated requirement-gathering sessions and analyzed business processes to identify optimization opportunities.
- Agile Collaboration: Employed agile methodologies, working closely with stakeholders to ensure alignment and responsiveness.
- Technical Support: Assisted senior management with technical analyses and deliverability assessments.
Maziyar K.
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
Eyasu H.
Last position:
Data Scientist at Deutsche Bundesbank
- Developed web scraping scripts to extract and parse over 5000 product data from the Zalando website.
- Performed ETL processes using Apache Spark in CDSW, loaded the data into the Hadoop ecosystem (HDFS), and managed data using Hive and Impala.
- Implemented machine learning algorithms, achieving 85–90% accuracy on multi-class product classification.
- Integrated Zalando's product and price data into the dashboard with Otto and Takko for interactive visuals.
Daniel C.
Last position:
Founder & Managing Director at BotCraft GmbH
- Building the company with a focus on connectivity for IIoT and Industry 4.0, iRPA/process automation, advanced robotics and smart systems, sensors and services
- Project management and software architecture for IoT gateway development (since 2020) with protocol translation, IT/OT convergence and GRC
- Developing RPA bots for automating and monitoring industrial processes with an agent-based AI approach (since 2020)
- Implementing unsupervised clustering and anomaly detection for time series data in big data streaming pipelines (since 2021)
- Introducing a Docker-based release train for OTA updates with DevSecOps and CI/CD (since 2018)
Discover over 15,000 top freelancers
Statistics of experts using Amazon Redshift
Aggregated from the professional profiles of matched freelancers.
Experience
15 years (Germany: 14 years)
Position duration
1.7 years (Germany: 1.9 years)
Positions per freelancer
12 (Germany: 9)
Top business areas
Information Technology, Business Intelligence, Project Management
Top industries
Information Technology, Automotive, Banking and Finance
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
100% (Germany: 98%)
Master's degree or higher
70% (Germany: 71%)
Doctorate
20% (Germany: 15%)
Certifications per freelancer
3 (Germany: 5)
Most common languages
English, German, Amharic
Speak two or more languages
90% (Germany: 97%)
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 Amazon Redshift
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.
Amazon Redshift 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 (70%)
- Automotive (60%)
- Banking and Finance (50%)
- Transportation (50%)
- Retail (50%)
- Education (40%)
- Manufacturing (40%)
- Professional Services (40%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Core purpose
Amazon Redshift is a fully managed cloud data warehouse for high-volume analytical workloads. It brings structured data from operational systems, applications and files into a central environment for reporting, business intelligence and complex SQL analysis. Teams use it to turn large datasets into reliable insights without managing traditional warehouse infrastructure.
Data warehouse design
Strong specialists shape Redshift around the company’s data model, query patterns and governance needs. They design star and snowflake schemas, select distribution and sort keys, and plan loading strategies that keep joins and scans efficient. They also define ownership, naming standards and workload priorities so the warehouse remains usable as data products grow.
AWS ecosystem
Redshift commonly works as part of an AWS analytics architecture rather than as an isolated database. Relevant components include S3 data lakes, AWS Glue Data Catalog, Redshift Spectrum, IAM, CloudWatch and streaming or orchestration services. Professionals may also connect dbt, Airflow, Fivetran, Matillion, Tableau, Power BI or Amazon QuickSight for transformation, scheduling and consumption.
- Ingest data from applications, APIs and operational databases
- Build ELT pipelines and governed analytical models
- Expose warehouse data to dashboards and reporting tools
- Combine warehouse tables with files in S3 through Spectrum
When expertise matters
Companies bring in freelance Redshift expertise during warehouse migrations, AWS data platform builds, reporting transformations and performance remediation. It is especially useful when queries slow down, pipelines become difficult to monitor, or teams need a clear path from raw data to trusted metrics. Munich teams can combine local workshops with remote delivery when collaboration, documentation and language expectations are agreed early.
Delivery and security
A capable professional handles more than SQL. They can configure workload management, analyze query plans, manage incremental loads, automate deployment and set up monitoring for failures and cost drivers. Secure delivery includes IAM roles, encryption, network controls, row or column access policies and documented recovery procedures, with attention to how Redshift integrates into the wider AWS account structure.
Judging quality
Look for practical evidence: a clear explanation of why a distribution key was chosen, how data quality is tested and how performance is measured before and after a change. Strong specialists distinguish Redshift from a transactional database and know when S3, a lakehouse pattern or another warehouse is a better fit. They leave maintainable SQL, repeatable pipelines and concise operational documentation.
Frequently asked questions
Not sure where to start with Amazon Redshift? These answers cover the essentials.
Amazon Redshift is used for cloud data warehousing, business intelligence, reporting and analytical SQL across data collected from many systems. It suits workloads that need centralized, governed access to historical and aggregated data rather than low-latency transaction processing.
Amazon Redshift is often considered when a company already relies on AWS services and wants close integration with S3, IAM, Glue and surrounding cloud controls. Snowflake and BigQuery may offer different approaches to storage, compute separation, operations or multicloud use, so the right choice depends on workload patterns, governance and the existing data estate.
A strong Amazon Redshift specialist usually understands advanced SQL, dimensional modeling, data quality and ELT design. Useful adjacent skills include Python, dbt, Airflow, AWS Glue, S3, IAM, infrastructure automation and dashboard tools such as Tableau or Amazon QuickSight.
The right level for Amazon Redshift depends on the work, not a fixed number of years. A focused SQL and modeling assignment may need a specialist who can work independently, while a migration or platform redesign calls for broader AWS knowledge, security awareness and ownership of production operations.
Amazon Redshift projects are often suitable for remote collaboration because the environment, code and documentation can be shared securely online. On-site sessions in Munich can still help with data discovery, stakeholder workshops and architecture decisions, while clear communication in the required business language keeps delivery aligned.
Ask an Amazon Redshift freelancer to explain a real warehouse design, including schema choices, distribution keys, sort keys, loading patterns and monitoring. Quality is visible in practical reasoning, safe change processes, tested pipelines and documentation that another professional can operate.
Amazon Redshift can query suitable data stored in Amazon S3 through Redshift Spectrum, which helps combine warehouse tables with data lake files. A specialist should still assess file formats, partitioning, permissions, data quality and query behavior before choosing an external-table approach.
Amazon Redshift may be a poor fit for transactional applications, highly irregular workloads or teams that do not need a centralized analytical warehouse. A qualified professional should compare the workload with services such as a lakehouse, another cloud warehouse or a relational database before recommending an implementation.
The average hourly rate of freelancers in Munich, Germany who have used Amazon Redshift in their recent projects is 105 €, which corresponds to a daily rate of about 839 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Amazon Redshift in their recent projects, 100% hold at least a Bachelor's degree, 70% hold at least a Master's degree, and 20% hold a doctorate.
On average, freelancers in Munich, Germany who have used Amazon Redshift in their recent projects have 15 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 Amazon Redshift in their recent projects are English (100%), German (90%), and Amharic (10%).
The most common industries among freelancers in Munich, Germany who have used Amazon Redshift in their recent projects are Information Technology (70%), Automotive (60%), and Banking and Finance (50%).
The most common business areas among freelancers in Munich, Germany who have used Amazon Redshift in their recent projects are Information Technology (90%), Business Intelligence (80%), and Project Management (70%).
Main locations of FRATCH Experts, who have recently used Amazon Redshift
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.
Berlin 
Frankfurt 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
