Apache Spark Experts in Austria
for scalable data processing, matched in minutes with vetted freelancersHire experts who process large datasets, build reliable ETL pipelines and tune Spark workloads across cloud and on-premise environments. FRATCH uses fast, precise AI matching to connect you with vetted, available freelancers who fit your project.
Meet FRATCH Experts in Austria, who have recently used Apache Spark
Alexander P.
Last position:
Owner & Lecturer at Own company for AI governance and data products, Vienna
- Consulting and interim management at the interface between IT operations and regulation
- Impact analysis and implementation planning for NISG 2026 and the EU AI Act, including risk management and reporting and evidence processes
- Training for governing bodies and employees on regulatory obligations
- Lectures in Data & Information Management and Human-Machine Interaction at University of Applied Sciences Burgenland, since 2023
- Supervision of master’s theses and participation in the examination board
- Presentations for business and educational institutions
- Design and development of data and AI products, platforms and pipelines
- Privacy-first architectures and zero-knowledge encryption, cloud-native on EU infrastructure
- MLOps and AIOps in live operations
- Own applications under own brand: shared codebase, separate delivery for each target device
- AI-assisted software development (vibe coding), complete agentic pipelines, code generation, implementation, automated testing, CI/CD and release cycles
- Publications on the EU AI Act, NIS2, DORA, CRA and CER as an integrated governance system
- Publications on data sovereignty, cloud economics and industrial image processing
- AI governance / compliance: data quality, Responsible AI, EU AI Act readiness, risk classification, AI ethics
Manuel P.
Last position:
AI Engineer at Misumi Europe GmbH & Motius GmbH
- Designed and built a next-generation NLP platform to accelerate sales-driven customer service through intelligent request analysis and routing, reducing average customer query response time by 30%.
- Architected a hybrid NLP system combining Large Language Models (LLMs) with traditional NLP pipelines for robust, explainable results.
- Developed request classification and routing mechanisms to accelerate customer support teams in handling customer queries faster and more accurately.
- Optimized LLM based data extraction and classification with context engineering.
- Integrated the platform into customer service processes, reducing response times and enhancing workforce efficiency.
Karl F.
Last position:
Managing Director at ONECEPT GmbH
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
Mario T.
Last position:
External Lecturer at FH Kufstein Tirol – University of Applied Sciences
- Study: Data Science & Intelligent Analytics
- Module: Big Data Processing
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 Apache Spark
Aggregated from the professional profiles of matched freelancers.
Experience
15 years
Position duration
2.2 years
Positions per freelancer
11
Top business areas
Business Intelligence, Information Technology, Product Development
Top industries
Information Technology, Manufacturing, Banking and Finance
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
100%
Master's degree or higher
89%
Doctorate
11%
Certifications per freelancer
4
Most common languages
German, English, French
Speak two or more languages
100%
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 Austria 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 Austria using Apache Spark
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.
Apache Spark 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%)
- Manufacturing (70%)
- Banking and Finance (60%)
- Education (50%)
- Retail (50%)
- Transportation (40%)
- Professional Services (40%)
- Media and Entertainment (30%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Distributed data processing
Apache Spark is an open-source analytics engine for processing large datasets across clusters. It supports batch workloads, streaming, interactive queries and machine learning through a unified programming model. Teams use it to turn raw data into reliable pipelines, analytical outputs and production services.
Spark ecosystem
Spark works with Scala, Java, Python and SQL, giving teams several ways to build and maintain data workloads. Its core modules include Spark SQL, Structured Streaming, MLlib and GraphX, while Delta Lake, Iceberg and Hive often support storage and table management. Strong specialists also understand Kafka, cloud object storage and containerised deployment.
Typical workloads
- Build batch and near-real-time ETL pipelines
- Transform event streams from Kafka and similar systems
- Create feature pipelines for machine learning models
- Optimise SQL queries over data lake and warehouse storage
- Migrate legacy Hadoop or MapReduce workloads to Spark
When expertise matters
Companies bring in freelance Apache Spark specialists when data volumes, processing delays or pipeline failures expose limits in an existing setup. They can design an ingestion strategy, improve partitioning and joins, establish testing, or prepare workloads for reliable cloud operation. In Austria, this can support manufacturing, finance, retail and research teams while allowing remote or on-site collaboration.
Delivery skills
A capable professional combines Spark knowledge with data modelling, distributed systems and production operations. They should be comfortable with schema evolution, fault tolerance, observability, security and cost-aware cluster configuration. Experience with Databricks, Kubernetes, AWS, Azure or Google Cloud can be useful when Spark workloads must run beyond a local environment.
Choosing a specialist
Look for evidence of production pipelines rather than familiarity with API examples alone. Ask how the professional handled skewed data, failed jobs, changing schemas and late-arriving events. Clear explanations of trade-offs, reproducible tests and measurable monitoring plans indicate a disciplined approach. For Austrian teams, confirm communication needs, working hours and the expected balance between remote delivery and on-site workshops.
Frequently asked questions
Need clarity? These are the questions we hear most often about Apache Spark.
Apache Spark is used to process and analyse large datasets across multiple machines. Companies use it for ETL, data lake transformations, streaming pipelines, SQL analytics and machine learning preparation.
Apache Spark offers a broader, more unified processing engine than Hadoop MapReduce. It can handle batch, streaming, SQL and machine learning workloads, while its in-memory and execution-planning features can reduce repeated data movement when the workload is suitable.
An Apache Spark specialist should usually understand Python or Scala, SQL, data modelling and distributed systems. Kafka, cloud storage, Delta Lake or Iceberg, orchestration tools and observability are also valuable for production delivery.
A small transformation pipeline may suit a professional who can work confidently with Spark SQL and DataFrames. Complex streaming, performance tuning or cluster migration calls for an Apache Spark specialist who has operated comparable workloads in production.
Yes. Apache Spark projects are often well suited to remote collaboration because code, configuration, tests and monitoring can be reviewed online. On-site workshops can still help with data ownership, security decisions and coordination with Austrian business teams.
Ask an Apache Spark freelancer to explain partitioning, shuffles, joins, schema changes and failure recovery in the context of your workload. Review testing practices, monitoring design and deployment history, not just sample notebooks or benchmark claims.
Apache Spark can complement a warehouse by handling ingestion, enrichment, large transformations and machine learning feature preparation before data reaches analytical tables. The right choice depends on latency, governance, storage design and the existing warehouse’s processing capabilities.
An Apache Spark expert should clarify source systems, data volume patterns, latency targets, storage formats, security rules and ownership of the finished pipelines. They should also agree on deployment, monitoring, documentation and how success will be verified.
The average hourly rate of freelancers in Austria who have used Apache Spark in their recent projects is 101 €, which corresponds to a daily rate of about 806 € based on an 8-hour working day.
Of the freelancers in Austria who have used Apache Spark in their recent projects, 100% hold at least a Bachelor's degree, 89% hold at least a Master's degree, and 11% hold a doctorate.
On average, freelancers in Austria who have used Apache Spark in their recent projects have 15 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 Apache Spark in their recent projects are German (100%), English (100%), and French (30%).
The most common industries among freelancers in Austria who have used Apache Spark in their recent projects are Information Technology (70%), Manufacturing (70%), and Banking and Finance (60%).
The most common business areas among freelancers in Austria who have used Apache Spark in their recent projects are Business Intelligence (100%), Information Technology (100%), and Product Development (70%).
Main locations of FRATCH Experts, who have recently used Apache Spark
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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