Amazon Redshift Experts in Munich
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Meet FRATCH Experts in Munich, who have recently used Amazon Redshift
Mirza Klimenta
Last position:
Agentic AI for a DeepResearch project at Freelance
- Created a multi-agentic system supported by a knowledge graph to automate drafting of research papers
- Used multiple experts (OpenAI models) collaborating during document drafting
- Extracted useful information from the knowledge graph
- Technologies: LangChain, LangGraph, Smolagents, LlamaIndex, dspy
- Infrastructure: Terraform and GitHub Actions (CI/CD) on AWS
- Deployed initial application as a Streamlit app
Suyash Shaha
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.
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.
Paul Webster
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.
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
Eyasu Habte
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 Carton
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
1.8 years (Germany: 2.3 years)
Positions per freelancer
12 (Germany: 8)
Top business areas
Business Intelligence, Information Technology, Product Development
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
75% (Germany: 66%)
Doctorate
25% (Germany: 14%)
Certifications per freelancer
4
Most common languages
English, German, Amharic
Speak two or more languages
88% (Germany: 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 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
Redshift basics
Amazon Redshift is AWS’s data warehouse service for analytics at scale. Teams use it to store structured data, run fast SQL queries, and power dashboards, reporting, and ad hoc analysis. It fits data-driven products that need clear access to business data, not transactional processing.
Typical work
- Design warehouse schemas and table layouts
- Build SQL models for reporting and analytics
- Set up ETL and ELT flows from source systems
- Tune queries, sort keys, and distribution styles
- Support BI tools that read from Redshift
Ecosystem skills
Strong Redshift professionals usually know AWS well: S3, IAM, Glue, Lambda, and sometimes Step Functions or Airflow. They also work comfortably with SQL, data modeling, and ingestion from databases, APIs, and files. Experience with Spectrum, materialized views, and workload management helps in larger setups.
When to bring in help
Companies call in freelance Redshift experts when a warehouse is slow, costs are rising, or the data model no longer fits the business. They also help during migrations from on-prem systems, Snowflake, or other warehouses to Amazon Redshift. In Munich, this often matters for analytics teams that need clear handover, secure access, and smooth collaboration with English-speaking stakeholders.
What good specialists do
A strong expert thinks in data flow, query patterns, and governance. They write clean SQL, document models, and test changes against real workloads. They also know when to use Redshift managed storage, when to unload data to S3, and when a design choice will hurt performance later.
Common project shapes
Redshift work often starts with a new warehouse, a migration, or a performance review. It can also include fixing broken loads, rebuilding fact and dimension layers, or preparing a reporting stack for finance, sales, or product teams. Good specialists leave behind stable pipelines, readable SQL, and a setup that other experts can maintain.
Frequently asked questions
Not sure where to start with Amazon Redshift? These answers cover the essentials.
Amazon Redshift is used for analytics, reporting, and warehouse-style data storage on AWS. Companies rely on it when they need fast SQL over large, structured datasets from many source systems. It is a fit for dashboards, business reporting, and data models that feed decisions.
Redshift is often chosen by teams already invested in AWS and wanting tight integration with S3, IAM, Glue, and other AWS services. Compared with Snowflake or BigQuery, the main questions are operating model, query patterns, and how much control the team wants over warehouse design. A good specialist can explain trade-offs without pushing a one-size-fits-all answer.
A strong Amazon Redshift specialist usually brings AWS knowledge, data modeling, ETL or ELT design, and performance tuning experience. Familiarity with S3, Glue, IAM, BI tools, and orchestration tools such as Airflow is often valuable. The best experts also understand governance, access control, and how data flows through the full stack.
Not every Amazon Redshift task needs a deep specialist, but schema design, migrations, and performance issues usually do. Smaller work such as a few queries or a simple load can be handled by a less senior expert if the surrounding setup is stable. The more sources, users, and reporting layers you have, the more experience matters.
Yes, most Amazon Redshift work can be done remotely because the core tasks are in SQL, cloud settings, and data pipelines. In Munich, remote work is often practical for design, tuning, and migration tasks, while workshops or handover sessions can be on-site if needed. Clear access to environments and data is more important than location.
A good Amazon Redshift expert explains design choices in plain language and can point to the effect on query speed, load stability, and maintainability. Look for evidence of warehouse modeling, performance tuning, and troubleshooting real pipeline issues. Strong answers mention trade-offs, not just tools.
Amazon Redshift projects often include analytics platforms, finance reporting, customer dashboards, and data consolidation from many systems. Teams also use it for migration from older warehouses or for rebuilding slow reporting layers. If the work depends on SQL-heavy analysis and AWS data services, Redshift is often central.
With Amazon Redshift, a skilled freelancer can usually become useful quickly if the schema, source systems, and access setup are clear. The first steps are often to inspect the warehouse design, review load jobs, and identify the biggest query or cost problems. Complexity rises fast when many teams share the same data model or governance rules.
The average hourly rate of freelancers in Munich, Germany who have used Amazon Redshift in their recent projects is 106 €, which corresponds to a daily rate of about 849 € 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, 75% hold at least a Master's degree, and 25% 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 1.8 years.
The most common languages among freelancers in Munich, Germany who have used Amazon Redshift in their recent projects are English (100%), German (88%), and Amharic (13%).
The most common industries among freelancers in Munich, Germany who have used Amazon Redshift in their recent projects are Information Technology (75%), Automotive (63%), and Banking and Finance (63%).
The most common business areas among freelancers in Munich, Germany who have used Amazon Redshift in their recent projects are Business Intelligence (88%), Information Technology (88%), and Product Development (63%).
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.
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Philipp Thomaschewski
FRATCH CEO
