PySpark Experts in Frankfurt
for scalable data processing, matched in minutes with vetted specialistsHire experts who process large datasets, build reliable Spark pipelines and connect PySpark with cloud data platforms, machine learning workflows and streaming systems. FRATCH matches you quickly and precisely with vetted, available freelance professionals.
Meet FRATCH Experts in Frankfurt, who have recently used PySpark
Monika T.
Last position:
Senior ETL Lead at Takeda GmbH
- Led design, development, and deployment of data solutions supporting a major pharma acquisition for Takeda Pharmaceutical Company, delivering transparency reporting systems across Azure,Databricks (Python and Shell Scripting) platforms.
- Owned,Designed and developed scalable ELT pipelines to process Customer and Product data using Azure, complex SQL, Databricks, and shell scripting, enabling efficient data integration and processing across multiple sources including job orchestration and workflow automation.
- Implemented performance optimization techniques (query tuning, parallelism, workload optimization), improving system efficiency and processing time.
- Applied strong analytical and problem-solving skills to assess technical solutions and support business requirements for compliance and transparency reporting.
- Designed scalable data foundations suitable for downstream analytics and AI workloads.
- Led data quality initiatives by assessing multiple source data, defining quality metrics, and establishing processes for monitoring and continuous improvement.
Ulm P.
Last position:
DataStage ETL Expert at ING Bank
- Datastage 11.7, dbt, Oracle 19, Python 3.12 / PySpark 3.5, Azure GitHub, Azure DevOps, Automic
- Development of migration jobs to transfer data from the collection DWH to the new Risk Mart, as well as development of ETL pipelines to migrate historical data from the old Mart to the new Risk Mart.
- Storage of the silver layer on Hadoop and the gold layer in Oracle.
- Translation of DataStage jobs into dbt to publish reporting data in Google Cloud to a PostgreSQL database.
- Creation and optimization of complex SQL queries for data extraction from a data vault, taking into account historical data in the point-in-time tables.
- Creation of Oracle table definitions (DDL) and adjustment of existing stored procedures.
- Versioning changes in GitHub and deployment via the CI/CD portal.
- Refactoring long-running DataStage jobs into Python using PySpark to reduce server load.
- Migration of SAS scripts to PL/SQL, including new development of distribution functions that have no direct equivalent in Oracle.
- Development of Automic jobs to run DataStage pipelines and Python scripts (PySpark jobs) that control the population of the SME and institutional risk tables in the Risk Mart and perform business calculations.
- Participation in the agile process, including creating user stories, estimations, and planning in Azure DevOps.
- Handling Azure DevOps tickets and close collaboration with testers and business teams for error analysis and resolution.
Ashkan Z.
Last position:
Microsoft Azure Senior Data Engineer / Senior Data Scientist at Vattenfall Europe
- Advising on the use of analytics and BI tools and services in the Microsoft Azure stack (e.g. MS Fabric, Synapse Workspaces and dedicated SQL pools, SQL Database, PostgreSQL, Snowflake, Databricks, Data Factory, SSIS, Analysis Services, Function Apps, Power BI, ML)
- Independently designing analytics solutions with Python, SQL, etc.
- Designing and implementing ETLs and data pipelines
- Creating and maintaining APIs
- Independently applying CI/CD, testing, and version control
- Data modeling
- Model development and optimization
- Anomaly detection with AI
- Predictive analytics
Used technologies:
- Snowflake
- Fabric
- Azure Synapse Analytics
- Azure DataFactory
- Azure Data Lake
- Azure DevOps
- Databricks
- Spark
- CI/CD
- SQL Database
- Python
- Power Platform
Alona L.
Last position:
AI Architect
AI-powered platform for automated UX validation and designer support
- Designed and led technical implementation of an enterprise-wide AI solution for automated UX review that improved design quality and significantly reduced manual review processes in teams
- Developed an automated UX validation tool as a Figma plugin and web application that generates test cases based on internal guidelines and reliably checks current designs for consistency and standard compliance
- Implemented an interactive designer chat based on RAG that answers questions about the current design and the company's UX guidelines, and designed the deployment architecture using containerized services
- Python, Azure OpenAI, PostgreSQL, REST API, Docker, OpenShift, Helm, CI/CD, Figma MCP, LLM, RAG, Prompt Engineering, GenAI, XAI, AI Architecture, AI Strategy
Anton R.
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)
Eduard V.
Last position:
Workshop Leader 'Introduction to AI Development Tools' at Software company in Wiesbaden
- Presentation introducing generic AI and large language models
- Explanation of legal frameworks (EU AI Act, US CLOUD Act, GDPR)
- Systematic review of AI tools along the SDLC and holistic systems
- Comparison of on-prem LLMs vs. cloud-based, as well as change management and works council
- Facilitated the discussion and derived next steps for introducing AI development tools
Tan P.
Last position:
DevOps Engineer in the DevOps Team at Rise-World
- Implementation of specified DevOps solutions to automate infrastructure (Terraform, Bicep, CloudFormation, Ansible) on-premises datacenter (Ovirt, Proxmox, Ceph Cluster, MinIO) and private cloud.
- Administration, configuration and implementation of CI/CD DevOps pipelines (GitLab, GitFlow) to support development process (Artifactory, Prometheus, Istio, service mesh, Helm Chart, OpenShift (Red Hat Enterprise) / Kubernetes cluster), Red Hat Satellite.
- Administration, setup, monitoring and patching of Linux infrastructure based on Red Hat Enterprise for Dev, Test and QA.
- Use of Scrum and Kanban methods.
- Administration, configuration and implementation of security standards for deploying on Dev, Test, QA and Prod stages of the new ePA applications.
- Development of new plugins and add-ons needed on current infrastructure.
- Database support.
- Data analytics support (Python, Spark, Pandas, Power BI, Splunk Enterprise).
- Implementation of best practices for DevSecOps and BizDevOps using GitOps (ArgoCD), Streamlit framework, Semaphore Ansible UI.
- Configuration and testing of iperf, uperf, sysbench using benchmark-operator for external source data and IoT/MDM devices, creating reports via ELK / OpenSearch.
- Building a new Databricks platform to collect and analyze big data from different sources and IoT devices into Hadoop framework (Python, Pandas, PySpark, Power BI, Apache Airflow).
- Building backend data aggregation and processing to automate configuration deployment between different OpenShift clusters and big data framework (Python, Pandas, PySpark, Apache Spark, PostgreSQL, Django 2, Ansible Automation, Jira JSM).
- Building a new ML pipeline platform using Kubeflow, TensorFlow, KServe.
- Data extraction, transformation and loading from different data sources including structured and unstructured data to analytic DWH / big data cluster using Python, Pandas, Polars, Power BI, Django backend and PostgreSQL.
- Setup of new DevOps Test and QA HashiCorp Vault cluster for PKI and IAM.
- Configuration and testing of automated patching based on CVSS score, SIEM-integrated CVEs.
- Use of Nexpose and InsightVM to scan vulnerability events in network, host, container and application.
- Design and implementation of secure and scalable AWS architectures including VPC, EC2, S3, RDS and Route53 and similar setups on Azure and GCP.
- Automated system provisioning and deployment using CloudFormation templates.
- Configuration of IAM roles, policies and permissions to ensure secure access control.
- Patch management, backup automation and disaster recovery setup on AWS infrastructure.
- Monitoring and optimization of system performance using AWS CloudWatch and AWS Trusted Advisor.
- Support of VMware services (vSphere, Aria, Horizon) and the virtual desktop environment.
- Development and maintenance of CI/CD pipelines using Jenkins, GitLab CI/CD and AWS CodePipeline with interface to Nutanix.
- Configuration of AWS CloudWatch to monitor application performance and system events.
- Planning and execution of migration of on-premises applications to AWS cloud platforms.
- Deployment of containerized applications using Docker and Kubernetes in AWS environments.
- Deployment of internal software packages between availability zones using AWS CodeDeploy.
- Building and deploying ML models using Scikit-learn, XGBoost and Spark MLlib including hyperparameter tuning, model evaluation and production deployment.
Roman K.
Last position:
Senior Data Engineer / Cloud Architect at DB Systel
- Development of a central billing app for cloud costs at DB
- AWS
- Python
- AWS CDK
- RDS
- Spark (PySpark)
- Glue
- Lambda
- CI/CD (GitLab)
- React/Typescript
- data optimization
- Scrum
Petru K.
Last position:
Architect & Technical Team Lead & Senior Developer at Goetel GmbH
- Design, architecture & development/programming of ETL/ELT data pipelines, DWH, BI solution
- Technical project lead, POC – proof-of-concept creation
- Liaison between business units and technical teams
- Azure DevOps Boards & Jira
- Data modeling & data engineering – data warehouse & data mart
- Azure (Data Factory, Azure SQL, Azure DevOps CI/CD, Azure Data Lake V2, Business Central REST API, OData API, OAuth2 tokens)
- SharePoint lists & API for ADF, Firebird DB, Postgres DB, DB2
- Power BI (Power Query), DAX, Excel PBI add-on, GIS data
- Automated ETL process monitoring/logging, performance monitoring, error monitoring – capturing & resolution
- Index performance tuning & statistics monitoring, Transact-SQL
- Data security – MFA (multi-factor authentication) & OAuth2, MS Graph, Azure networks & firewalls, gateways, roles, user groups – with read/write permissions
- Sources – Vario Bill, Camunda, Radius, Geo Database, OTRS, PAST, MS Dynamics Business Central, Azure Blob Data Lake, SharePoint lists
Discover over 15,000 top freelancers
Statistics of experts using PySpark
Aggregated from the professional profiles of matched freelancers.
Experience
18 years (Germany: 13 years)
Position duration
2.2 years (Germany: 2.8 years)
Positions per freelancer
17 (Germany: 10)
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Healthcare, Banking and Finance
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
100% (Germany: 96%)
Master's degree or higher
57% (Germany: 71%)
Doctorate
14% (Germany: 15%)
Certifications per freelancer
5 (Germany: 4)
Most common languages
German, English, French
Speak two or more languages
100% (Germany: 96%)
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 Frankfurt 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 Frankfurt using PySpark
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.
PySpark 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 (89%)
- Healthcare (67%)
- Banking and Finance (56%)
- Energy (44%)
- Transportation (44%)
- Manufacturing (44%)
- Professional Services (44%)
- Retail (44%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What PySpark does
PySpark is the Python interface for Apache Spark, a distributed processing framework for large-scale data. It lets teams transform structured and semi-structured data across clusters while using familiar Python syntax. Companies use it for batch processing, analytics, feature preparation and data quality workflows.
Core processing model
PySpark works with DataFrames, Spark SQL and resilient distributed processing across multiple machines. Strong specialists understand lazy evaluation, joins, partitions, caching, shuffles and query plans. They select the right transformations and actions so workloads remain reliable as data volumes and business rules change.
Ecosystem and tooling
PySpark projects often connect several parts of a modern data stack:
- Spark SQL and DataFrames for transformations and analytical queries
- Structured Streaming for near-real-time data pipelines
- MLlib for distributed machine learning workflows
- Delta Lake, Iceberg or Parquet for dependable storage
- Databricks, Kubernetes or cloud Spark services for execution
Python expertise also matters. Professionals may work with pandas, SQL, Airflow, Kafka, object storage and monitoring tools, depending on the wider platform.
Where companies use it
PySpark supports recommendation data, fraud detection, customer analytics, log analysis, financial reporting and industrial telemetry. It is useful when local processing no longer handles the workload or when one pipeline must combine data from many systems. In Frankfurt, teams across finance, logistics, manufacturing and other data-intensive industries may use it in cloud or hybrid environments.
When freelance expertise helps
Companies often bring in a freelance specialist when a Spark migration is blocked, a pipeline is too slow or a new lakehouse needs a dependable foundation. External expertise can also help with cluster configuration, cost control, testing, orchestration and production handover. Remote work is common, while Frankfurt-based collaboration can support workshops, stakeholder access and German or English communication.
What strong specialists deliver
Strong PySpark professionals make processing logic readable, testable and observable. They profile workloads instead of guessing, explain trade-offs between Spark and alternatives such as pandas, SQL warehouses or Flink, and design for failure recovery. Look for practical evidence: clear data contracts, efficient partitioning, measured query improvements, automated tests and documentation that lets the internal team operate the result.
Frequently asked questions
Not sure where to start with PySpark? These answers cover the essentials.
PySpark is used to process and transform large datasets across distributed computing clusters. Typical work includes data lake pipelines, reporting preparation, event processing, feature engineering and batch analytics.
PySpark is suited to distributed workloads that exceed the practical limits of a single machine. pandas is often simpler for smaller in-memory datasets, while SQL warehouses can be preferable when data already sits in a highly optimized analytical database.
A strong PySpark specialist commonly works with Python, SQL, cloud storage and orchestration tools such as Airflow. Kafka, Docker, Kubernetes, Delta Lake, Iceberg, data quality testing and monitoring are also valuable depending on the system.
The right level depends on the workload, production risk and existing platform. For a simple transformation, focused PySpark experience may be enough; cluster tuning, streaming, lakehouse design or migration work calls for a professional who has handled those conditions in production.
Yes. PySpark work is well suited to remote collaboration through shared repositories, cloud environments, issue tracking and automated testing. Frankfurt teams should define access controls, meeting routines and whether German, English or both are needed for stakeholder communication.
PySpark is often a strong fit for batch processing, unified analytics and teams already using the Spark ecosystem. Apache Flink may be preferable when low-latency event processing and advanced streaming semantics are the central requirements.
Ask a PySpark professional to explain partitioning, join strategy, failure handling and the evidence behind performance decisions. Review tests, data validation, observability, deployment practices and whether the pipeline remains understandable for the team taking ownership.
A PySpark freelancer should clarify data sources, expected volumes, freshness targets, schema changes, security rules and the execution environment. They should also confirm ownership of orchestration, monitoring, documentation and handover before proposing an implementation.
The average hourly rate of freelancers in Frankfurt, Germany who have used PySpark in their recent projects is 103 €, which corresponds to a daily rate of about 824 € based on an 8-hour working day.
Of the freelancers in Frankfurt, Germany who have used PySpark in their recent projects, 100% hold at least a Bachelor's degree, 57% hold at least a Master's degree, and 14% hold a doctorate.
On average, freelancers in Frankfurt, Germany who have used PySpark in their recent projects have 18 years of professional experience, with a single engagement typically lasting around 2.2 years.
The most common languages among freelancers in Frankfurt, Germany who have used PySpark in their recent projects are German (100%), English (100%), and French (33%).
The most common industries among freelancers in Frankfurt, Germany who have used PySpark in their recent projects are Information Technology (89%), Healthcare (67%), and Banking and Finance (56%).
The most common business areas among freelancers in Frankfurt, Germany who have used PySpark in their recent projects are Information Technology (100%), Business Intelligence (89%), and Product Development (78%).
Main locations of FRATCH Experts, who have recently used PySpark
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 
Munich 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
