Apache Parquet Experts in Germany
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Meet FRATCH Experts in Germany, who have recently used Apache Parquet
Volker Haase
Last position:
Data Analyst at Optaro GmbH
Creation of workflows for generating the data basis for the article import of a web shop: combining data from several sources, analyzing the requirements, designing the process with Jupyter Notebooks and Knime. Also creating code for automation in Python using Polars and Pandas.
Processing the source data, filtering and merging the source files and creating the needed intermediate products, creating the upload files, plausibility checks, quality checks.
Technologies used: PyCharm, Python, Jupyter Notebooks, SQL, Knime. Pandas, Polars
Nune Isabekyan
Last position:
Fractional CTO at OpsWorker
OpsWorker turns Kubernetes alerts into root-cause analyses, on top of the monitoring a team already runs. I lead the technical side: the agent architecture, the AWS infrastructure it runs on (fully inside EU regions), and the engineering decisions behind it, read-only in the cluster by default, human in the loop for judgment. The stack underneath: Amazon Bedrock and Bedrock AgentCore, agents built with the Strands Agents SDK, the Claude and OpenAI APIs, and the Kubernetes API.
Daniel Martinez Maqueda
Last position:
Founding Database Engineer at tonbo.io
Working on the next iteration of tonbo to make it the most flexible in-process analytical database in the market that scales and is operated with strong availability
Introduced object scope cache to the remote storage layer to avoid I/O churn
Working on refactoring WAL to support remote storage
Taking care of the health of the systems as well as designing the operational story and bringing them to production
Technologies: LSM, WAL, Arrow, Parquet, Rust
Basil Sattler
Last position:
Senior Developer / Data Engineer at Large energy-sector company
- Co-founded the Real-Time Data team, which grew to 10 members over time.
- Developed and delivered core data products.
- Optimized real-time application performance and implemented monitoring, alerting and logging solutions to ensure system stability.
- Created and maintained deployment pipelines.
- Collaborated with teammates, architects and experts in an agile Scrum environment.
- Operated applications, analyzed, tested and troubleshot software solutions.
Domenik Jones
Last position:
Python Engineer and Cloud Migration Consultant at Unknown
- Supported the company's transition from an on-premise architecture to AWS cloud services, modernizing infrastructure and optimizing operational efficiency.
- Leveraged expertise in automation and software implementation to enhance scalability, reliability and profitability.
- Implemented Poetry and Ruff to streamline Python dependency management and code quality checks, improving development efficiency and reducing errors.
- Implemented an automated CI/CD strategy with GitHub Actions, which decreased deployment times and minimized manual intervention.
- Enforced deployment automation for Kubernetes, enhancing the scalability and reliability of applications across the organization.
- Evaluated and implemented Apache Airflow for workflow management, leading to more efficient scheduling and monitoring of data pipelines.
- Created data interfaces for energy traders, enabling them to optimize profit margins through improved data analysis and decision-making tools.
Moritz Kath
Last position:
Senior DevOps Engineer GCP at tedi GmbH & Co. KG
- Design and implementation of DevOps and CI/CD practices for data and analytics teams
- Introduction of infrastructure as code with Terraform (IaC)
- Setup and maintenance of GCP user and permission management with Terraform in multi-project environment
- Design and implementation of CI/CD pipelines with GitHub
- Leading and training developer team for the introduction of IaC and CI/CD practices
- Building and optimising database connectors with Apache Arrow for terabyte scale data extraction (Oracle, SAP)
- Optimising data lake storage and warehouse ingest
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.
Friedhelm Matten
Last position:
Project at Deutsche Gesetzliche Unfallversicherung
- Collaborated on HL7 FHIR profiling and validation
- Created CodableConcepts (code/value systems), extensions and profiles like Composition and Bundle
- Extensive instance creation and validation
- Used Simplifier.net, Forge and the FHIR Java Validator
Arman Abouali
Last position:
Research Associate (Machine Learning & Signal Analysis) at Technical University of Clausthal
- Built end-to-end pipelines for sensor-driven time-series vibration signals: data cleaning, feature engineering, model selection for unsupervised learning, and validation for size classification tasks.
- Applied statistical feature extraction to characterize dynamic responses.
- Developed Python algorithms for signal processing (FFT, PSD) and classification; compared multiple model architectures with uncertainty-aware evaluation.
- Supervised students in labs and projects (analysis methods, interpretation, reporting); delivered a practical laboratory course and contributed to documentation and presentations.
- Prepared academic outputs: Master’s thesis and conference contribution (SimScience 2025).
Kashaf Khan
Last position:
AI Consultant / Expert at Siemens Mobility
- Evaluated 45+ AI use cases and developed a prioritization framework for Siemens’ internal AI roadmap.
- Tested internal tools like DRIM, SiemensGPT, Microsoft Copilot; presented evaluation outcomes to stakeholders.
- Identified performance gaps and improved tool adoption by 65%.
- Supported AI knowledge-sharing initiatives, led tool onboarding sessions, and improved team AI literacy.
- Collaborated with engineering, procurement, and digital teams on tool feedback and strategy alignment.
Christian Richter
Last position:
Freelance Data Engineer at Ingenieurbüro Christian Richter – Data, Cloud & Container
- Contributed to over 20 successful projects
Mario Gmbh
Last position:
Software and Data Engineer at Plexify GmbH
- Architecture, design and development of an MVP application for a provider of specialized travel experiences
- Technologies: Python, FastAPI, Firestore, Firebase, Docker
Anton Rösler
Last position:
AI-Engineer at Publicly traded company, industrial safety technology
- Designed and implemented the agent-based AI architecture for a company-wide platform to securely deploy LLM-based agents
- Designed and implemented end-to-end RAG pipelines from multiple sources: document preprocessing, chunking strategies for different document types, embeddings, retrieval with re-ranking, and robust prompt orchestration
- Developed a modular context engineering framework with skill architecture, context isolation, and dynamic resource management; human-in-the-loop control for enterprise tool integrations
- Built the CI/CD pipeline, testing strategy, tracing on the software side as well as automated LLM and agent evaluations, red team testing and tracing, and handed over to a reproducible production environment (ISO27001 and SOC2 compliant)
Matthias Von Görbitz
Last position:
Managing Partner at IT consulting firm
Discover over 15,000 top freelancers
Statistics of experts using Apache Parquet
Aggregated from the professional profiles of matched freelancers.
Experience
19 years
Position duration
3.6 years
Positions per freelancer
11
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Retail, Automotive
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
82%
Master's degree or higher
55%
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 Germany 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 Germany using Apache Parquet
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
Modern Columnar Storage for Big Data
Apache Parquet is an open-source, column-oriented storage file format designed for efficient data processing. Unlike row-oriented formats, it organizes data by column, which significantly speeds up analytical queries and reduces storage footprints. It is a cornerstone of modern data lake architectures and cloud storage solutions.
Driving Data Architecture in German Enterprises
German organizations in automotive, finance, and logistics handle massive volumes of telemetry and transactional data. Specialists implement this format to optimize these pipelines, enabling fast business intelligence and reporting.
- Building scalable data lakes on AWS, Azure, or local cloud providers.
- Accelerating SQL-on-Hadoop and cloud query engines.
- Archiving historical enterprise data cost-effectively.
Seamless Integration with Big Data Frameworks
The format is highly integrated with the broader data engineering ecosystem. Professionals utilize it alongside processing engines like Apache Spark, Apache Flink, and query engines like Trino, Presto, and AWS Athena. It natively supports complex nested data structures while maintaining compression benefits.
Performance Engineering through Compression
Achieving the full benefits of columnar storage requires deep technical knowledge. Experts configure dictionary encoding, run-length encoding, and bit-packing to minimize storage costs. They also fine-tune row group sizes and page sizes to align with specific query patterns, drastically reducing I/O operations.
Indicators Your Organization Needs External Expertise
Many companies face performance bottlenecks or soaring cloud storage bills as their data grows. Bringing in an external specialist helps resolve these inefficiencies quickly.
- Query response times in your analytical database are degrading.
- Cloud storage and data transfer costs are rising unexpectedly.
- Data pipelines are failing due to inefficient schema evolution.
What Defines an Expert in This Domain
Top-tier professionals combine deep storage format knowledge with broader data platform engineering skills. They understand how different execution engines read metadata, how to design robust schemas, and how to transition legacy systems. Their expertise ensures your data platform remains scalable, performant, and cost-efficient.
Frequently asked questions
Need clarity? These are the questions we hear most often about Apache Parquet.
Utilizing Apache Parquet allows companies to drastically reduce storage costs through advanced compression techniques like snappy or gzip. Because it is a column-oriented format, analytical query engines only read the specific columns needed, which accelerates query performance and minimizes network I/O.
While CSV and JSON are human-readable, row-based, and highly inefficient for large datasets, Apache Parquet is a binary, columnar format designed specifically for big data analytics. It stores metadata within the file, allowing query engines to skip irrelevant data blocks, which is impossible with text-based formats.
A qualified expert in Apache Parquet must possess a strong background in data engineering, specifically with tools like Apache Spark, Presto, or AWS Athena. They need a deep understanding of schema design, partitioning strategies, and compression algorithms to structure data lakes effectively.
Because Apache Parquet is optimized for read-heavy analytical workloads, it is write-once and immutable. Making frequent updates or row-level inserts is highly inefficient and usually requires rewriting entire partitions or leveraging frameworks like Delta Lake or Apache Iceberg.
In Germany, industries like automotive, manufacturing, and fintech generate vast streams of IoT and transactional data that require optimization. Enlisting specialists in Apache Parquet helps these firms build high-performance reporting pipelines while complying with local data privacy guidelines.
Yes, data engineering tasks involving Apache Parquet are highly suited for remote collaboration. Since the work revolves around cloud data pipelines and storage architecture, specialists can seamlessly integrate with your team using modern cloud environments and collaborative coding platforms.
When using Apache Parquet, professionals manage schema evolution by defining clear backward and forward compatibility rules. They configure query engines to merge schemas automatically or leverage external metastores to track changes without breaking existing downstream pipelines.
To assess a candidate, look at their practical experience in designing large-scale data lakes and optimizing query performance. A competent Apache Parquet specialist should be able to explain how they designed partitioning strategies and handled data skew in previous projects.
The average hourly rate of freelancers in Germany who have used Apache Parquet in their recent projects is 103 €, which corresponds to a daily rate of about 820 € based on an 8-hour working day.
Of the freelancers in Germany who have used Apache Parquet in their recent projects, 82% hold at least a Bachelor's degree and 55% hold at least a Master's degree.
On average, freelancers in Germany who have used Apache Parquet in their recent projects have 19 years of professional experience, with a single engagement typically lasting around 3.6 years.
The most common languages among freelancers in Germany who have used Apache Parquet in their recent projects are German (100%), English (100%), and Spanish (14%).
The most common industries among freelancers in Germany who have used Apache Parquet in their recent projects are Information Technology (93%), Retail (57%), and Automotive (50%).
The most common business areas among freelancers in Germany who have used Apache Parquet in their recent projects are Information Technology (100%), Business Intelligence (86%), and Product Development (86%).
Main locations of FRATCH Experts, who have recently used Apache Parquet
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 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
