PySpark Expert in Austria
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Meet FRATCH Experts in Austria, who have recently used PySpark
Stefan D.
Last position:
BI Consultant in Controlling at Reutter GmbH
- Extraction, transformation, and cleansing of data from Microsoft Dynamics AX
- Creation of sales reports in Power BI
- Training employees in business intelligence
- Technologies: Power BI, SQL, SQL Server Integration Services (SSIS)
Stefania D.
Last position:
Data Engineer at Storebox
Tech: AWS (Glue, Lambda, Redshift), Airflow, PostgreSQL, Python, PySpark, Metabase, Power BI
Delivered: Analytics-Ready Data Models • Legacy SQL to Cloud ETL Migration • Dynamic Pricing Engine
- Owned and evolved the company data warehouse end-to-end — from ingestion to transformation to analytics-ready dimensional data models on AWS Redshift.
- Collaborated with Analysts, Data Scientists, and business stakeholders to deliver scalable dimensional data models that enable self-serve analytics and streamline dashboarding in Metabase and Power BI.
- Architected end-to-end ETL/ELT pipelines on AWS (Glue, Lambda, Redshift) using Python and PySpark, orchestrated with Apache Airflow (MWAA) for reliability and observability.
- Defined and enforced data quality standards and governance practices across pipelines and the core data layer.
- Led migration of legacy SQL infrastructure into scalable AWS Glue pipelines with distributed PySpark processing, eliminating bottlenecks and reducing downtime.
- Developed a dynamic pricing engine applying automated promotional discounts based on occupancy rates, competitor pricing, and location performance tiers.
- Designed schema mappings to ingest MongoDB data into structured relational systems (Redshift/PostgreSQL).
Marcel S.
Last position:
Senior AI Engineer - Python at Insurance Company
Project Tech Stack: Python, AWS, Azure, FastAPI, openai, pandas, unittest/pymock
Achievements:
- Engineered automated data extraction pipelines to transform complex Excel datasets into structured formats via LLM-driven workflows.
- Architected a generative slide-deck engine that translates natural language prompts into formatted presentation assets.
- Integrated advanced LLM capabilities with the OpenAI Response API, implementing sophisticated tool-calling and structured output logic.
- Developed and containerized scalable backend microservice using FastAPI, Docker, and OpenShift to host and serve agentic skills.
Fabio G.
Last position:
IT Architect, Requirements Analyst and Consultant at CANCOM
- Supports CANCOM customers in migrating legacy on-prem systems to Microsoft Fabric and Microsoft Foundry
- Takes over and stabilizes existing solutions after a short handover
- Business analysis and requirements engineering for migration to a new cloud environment
- Optimization of machine learning models for feature extraction and customer profiling
- Ensures data protection and compliance
- Leads the migration of on-prem systems to Microsoft Fabric
- Designs new AI platforms for clients
- Tests the integration of chatbots for document intelligence with Microsoft Foundry, including requirements analysis, implementation, validation, and client communication
Hossein A.
Last position:
Datawarehouse Consultant at LENZING AG
- Consulting on the enterprise data-warehouse and reporting practice at one of Austria's largest industrial groups.
- Designing and standardizing Power BI dashboards and data-visualization governance for company-wide enterprise reporting.
- Developing a WCAG-compliant, colorblind-safe visualization standard (Okabe-Ito palette) to harmonize dashboards across the organization.
Gasper Z.
Last position:
Senior BI Consultant at PMONE GmbH
Discover over 15,000 top freelancers
Statistics of experts using PySpark
Aggregated from the professional profiles of matched freelancers.
Experience
13 years
Position duration
2.2 years
Positions per freelancer
9
Top business areas
Business Intelligence, Information Technology, Project Management
Top industries
Banking and Finance, Manufacturing, Information Technology
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
100%
Master's degree or higher
83%
Doctorate
17%
Certifications per freelancer
4
Most common languages
German, English, Persian
Speak two or more languages
100%
Based on our profile pool as of 16 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Austria are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging within that range.
Average rates of experts in Austria 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 16 Oct 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.
- Banking and Finance (67%)
- Manufacturing (67%)
- Information Technology (50%)
- Transportation (50%)
- Professional Services (33%)
- Real Estate (33%)
- Retail (33%)
- Biotechnology (17%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Distributed data processing
PySpark is the Python interface for Apache Spark, a framework for processing large datasets across distributed computing environments. It lets teams transform, join and aggregate data in parallel instead of relying on a single machine. Professionals use it for batch processing, exploratory analysis and production data workflows.
Pipelines and workloads
PySpark appears in data platforms that ingest files, event streams and database records before preparing them for analytics or machine learning. Typical deliverables include reliable ETL pipelines, reusable transformation jobs, data quality checks and partitioned outputs for warehouses or lakehouses. Its DataFrame API supports readable transformations while Spark SQL enables familiar query patterns.
- Build batch and incremental ingestion pipelines
- Clean, join and enrich structured datasets
- Prepare features for machine learning workflows
- Tune jobs for memory, shuffle and partition behavior
Ecosystem and tooling
Strong PySpark specialists work across Apache Spark, Python, Spark SQL and common storage formats such as Parquet and Delta Lake. They may also use Kafka for streaming, Airflow for orchestration and notebooks for investigation. Experience with Databricks, Kubernetes or cloud services helps when Spark workloads run in managed or containerized environments.
When expertise matters
Companies bring in freelance expertise when pipelines become slow, costly or difficult to maintain, or when a proof of concept must become a dependable service. A specialist can review execution plans, correct skewed joins, improve partitioning and establish testing and deployment practices. In Austria, this can support data work across manufacturing, finance, retail and public services while fitting remote or on-site collaboration.
- Migrate workloads from local Python processing to Spark
- Reduce failures in scheduled production jobs
- Introduce streaming or near-real-time processing
- Connect Spark with a warehouse, lakehouse or cloud platform
What strong professionals deliver
Quality work starts with a clear data contract, measurable pipeline behavior and an understanding of the underlying cluster. Strong professionals design idempotent jobs, manage schema changes and separate business logic from infrastructure settings. They also explain trade-offs between performance, reliability and operational simplicity instead of tuning blindly.
Selecting the right specialist
Look for evidence of production PySpark work, not only notebook experiments. Ask how the professional diagnosed slow stages, handled late or duplicate data and validated results after a migration. Practical knowledge of Python testing, SQL, data modeling, Git and CI/CD is valuable because PySpark jobs must remain understandable and supportable after delivery.
Frequently asked questions
Everything clients usually want to know about PySpark, in one place.
PySpark is used to process and transform large datasets across a cluster with Python. Companies use it for batch ETL, data lakehouse preparation, streaming workflows, analytics and machine learning feature preparation.
PySpark distributes processing across multiple machines, while pandas is usually optimized for data that fits on one machine. pandas can be simpler for smaller datasets, but PySpark is better suited to large volumes, repeatable pipelines and cluster-based production workloads.
PySpark provides access to Apache Spark through Python and works well where teams already use Python for analytics or machine learning. Scala can offer closer access to Spark internals, while SQL may be clearer for relational transformations; the right choice depends on the team and workload.
A strong PySpark professional often brings Python, SQL, data modeling and distributed-systems knowledge. Experience with Kafka, Airflow, Delta Lake, cloud storage, Databricks, testing and CI/CD can also be important for production delivery.
The required level depends on the workload, data volume and operational risk. A straightforward transformation may need solid Spark and SQL knowledge, while streaming, cluster tuning or a lakehouse migration calls for a professional with proven production experience.
PySpark work is often suitable for remote collaboration because code, notebooks, pipelines and cloud environments can be shared securely. On-site work in Austria may still help during workshops, data access reviews or close coordination with teams that handle sensitive systems.
Ask the professional to explain a real performance issue, including the cause, evidence and remedy. Good PySpark work includes tested transformations, clear schemas, sensible partitioning, monitored jobs and documented decisions rather than only a working notebook.
Before starting with PySpark, clarify data sources, expected volumes, delivery frequency, cluster environment, access rights and ownership of the resulting code. Also confirm data quality rules, failure handling, deployment expectations and how success will be measured.
The average hourly rate of freelancers in Austria who have used PySpark in their recent projects is 97 €, which corresponds to a daily rate of about 773 € based on an 8-hour working day.
Of the freelancers in Austria who have used PySpark in their recent projects, 100% hold at least a Bachelor's degree, 83% hold at least a Master's degree, and 17% hold a doctorate.
On average, freelancers in Austria who have used PySpark in their recent projects have 13 years of professional experience, with a single engagement typically lasting around 2.2 years.
The most common languages among freelancers in Austria who have used PySpark in their recent projects are German (100%), English (100%), and Persian (17%).
The most common industries among freelancers in Austria who have used PySpark in their recent projects are Banking and Finance (67%), Manufacturing (67%), and Information Technology (50%).
The most common business areas among freelancers in Austria who have used PySpark in their recent projects are Business Intelligence (100%), Information Technology (100%), and Project Management (67%).
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.
Countries:
- Germany
- Austria
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