Apache Spark Experts in Vienna
for high-performance data work, matched in minutes with vetted specialistsHire experts who process large datasets, build reliable ETL pipelines and develop machine learning workflows with Apache Spark, PySpark and Delta Lake. FRATCH connects you with precise matches from vetted, available freelancers quickly.
Meet FRATCH Experts in Vienna, who have recently used Apache Spark
Alexander P.
Last position:
Owner & Lecturer at Artellico / Own Company for AI Governance and Data Products
- Consulting and interim management at the intersection of IT operations and regulation
- Impact analysis and implementation planning for NISG 2026 and EU AI Act, including risk management and reporting and evidence processes
- Training for governing bodies and employees on regulatory obligations
- Teaching assignments in Data & Information Management and Human-Machine Interaction at Hochschule Burgenland, since 2023
- Supervision of master’s theses and participation in the examination board
- Expert presentations in 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
- Connection to bank account systems via SEPA direct debit mandates and message formats according to ISO 20022 (pain.008, camt.053)
- MLOps and AIOps in ongoing operations
- Own applications under own brand: shared code base, 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 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.
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
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
17 years
Position duration
2.2 years
Positions per freelancer
11
Top business areas
Business Intelligence, Information Technology, Project Management
Top industries
Banking and Finance, Information Technology, Manufacturing
Certification focus areas
Information Technology, Project Management, Business Intelligence
Bachelor's degree or higher
100%
Master's degree or higher
100%
Doctorate
14%
Certifications per freelancer
3
Most common languages
German, English, Spanish
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 Vienna 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 Vienna 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 16 Oct 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.
- Banking and Finance (71%)
- Information Technology (71%)
- Manufacturing (71%)
- Professional Services (57%)
- Education (43%)
- Healthcare (43%)
- Transportation (43%)
- Media and Entertainment (43%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Distributed Data Processing
Apache Spark is an open-source engine for processing large datasets across clusters. It supports batch workloads, streaming, SQL analytics and machine learning through a unified programming model. Teams use it when one machine cannot handle the volume, speed or complexity of their data.
Core Languages
Spark professionals commonly work with Scala, Python through PySpark, Java and SQL. Strong delivery depends on understanding Spark DataFrames, RDDs, lazy evaluation, joins, partitioning and execution plans. They also connect Spark to storage and messaging systems such as object storage, Kafka and relational databases.
Data Pipelines
Apache Spark appears in ingestion, transformation and enrichment pipelines for data lakes and warehouses. Experts design jobs that handle schema changes, late data, retries and incremental processing without creating duplicate records. They may also implement Delta Lake tables, quality checks, lineage and orchestration with Airflow or similar tools.
Typical Workloads
- Build batch pipelines for logs, transactions and operational data
- Process real-time events with Structured Streaming
- Prepare features and training data for machine learning
- Optimize SQL queries and cluster workloads
- Migrate legacy Hadoop MapReduce jobs to Spark
When Expertise Matters
Companies bring in freelance Spark specialists when pipelines are slow, unstable or costly to operate. They help during cloud migrations, lakehouse projects, streaming launches and analytics platform redesigns. In Vienna, remote collaboration is common, while on-site work can help when data teams need close coordination across German- and English-speaking stakeholders.
What Strong Specialists Deliver
Experienced professionals profile workloads before changing code. They tune partitions, avoid unnecessary shuffles, choose suitable file formats and use caching only when it helps. They also test data contracts, monitor job health and explain trade-offs clearly, so a Spark solution remains maintainable after handover.
Frequently asked questions
Curious about Apache Spark? Here are the answers that come up again and again.
Apache Spark is used for distributed data processing across batch, streaming, SQL and machine learning workloads. Companies use it to transform data lakes, analyze event streams, prepare features and run large-scale ETL pipelines.
Apache Spark keeps a broader processing model in memory and offers APIs for SQL, streaming and machine learning, while Hadoop MapReduce is centered on disk-based batch jobs. Spark is often preferred for iterative or interactive workloads, but the right choice depends on data volume, latency needs and the existing Hadoop environment.
A strong Apache Spark specialist often brings Scala or PySpark, SQL, cloud storage and distributed-systems knowledge. Experience with Kafka, Delta Lake, Airflow, Kubernetes, Databricks and data quality practices can also be important for production delivery.
Apache Spark work can range from a focused pipeline change to a full data-platform redesign. The required expertise depends on cluster operations, streaming guarantees, governance and performance constraints, so review comparable deliverables rather than relying only on a generic profile.
Apache Spark projects are often suitable for remote collaboration because code, orchestration and cloud environments are shared digitally. Vienna-based teams may still prefer on-site workshops for architecture decisions, handovers or coordination across German- and English-speaking stakeholders.
Apache Spark is a strong fit when processing needs extend beyond warehouse SQL, such as large files, complex transformations, streaming or custom machine learning preparation. A warehouse tool may be simpler for SQL transformations that already run efficiently inside the warehouse.
Look for evidence of production pipelines, not only familiarity with APIs. A capable Apache Spark professional can explain partition sizing, shuffle behavior, schema handling, failure recovery, observability and how they validated both performance and data correctness.
An Apache Spark freelancer may deliver batch or streaming jobs, reusable transformation modules, deployment configuration, tests and monitoring dashboards. They should also document data assumptions, operational runbooks and the decisions needed for the team to maintain the workload.
The average hourly rate of freelancers in Vienna, Austria who have used Apache Spark in their recent projects is 100 €, which corresponds to a daily rate of about 799 € based on an 8-hour working day.
Of the freelancers in Vienna, Austria who have used Apache Spark in their recent projects, 100% hold at least a Bachelor's degree, 100% hold at least a Master's degree, and 14% hold a doctorate.
On average, freelancers in Vienna, Austria who have used Apache Spark in their recent projects have 17 years of professional experience, with a single engagement typically lasting around 2.2 years.
The most common languages among freelancers in Vienna, Austria who have used Apache Spark in their recent projects are German (100%), English (100%), and Spanish (29%).
The most common industries among freelancers in Vienna, Austria who have used Apache Spark in their recent projects are Banking and Finance (71%), Information Technology (71%), and Manufacturing (71%).
The most common business areas among freelancers in Vienna, Austria who have used Apache Spark in their recent projects are Business Intelligence (100%), Information Technology (100%), and Project Management (71%).
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:
Vienna 
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