Apache Spark Experts in Berlin
for scalable data processing, matched in minutes with vetted freelancersHire experts who build reliable batch and streaming pipelines, tune Spark SQL workloads, and connect data lakes with cloud platforms. FRATCH matches you quickly and precisely with vetted, available freelancers for your Apache Spark project.
Meet FRATCH Experts in Berlin, who have recently used Apache Spark
William N.
Last position:
Power BI Solutions Architect/Engineer & AI Consultant at AVERDUNG GmbH
- Redesign of the company's BI infrastructure: replacement of a fragmented landscape of manually maintained Excel solutions and CSV imports with a centralized Power BI environment featuring a unified data model as the company-wide single source of truth
- Consolidation of previously isolated reporting logic into a central semantic model – eliminating redundant files, manual data transfers, and inconsistent metrics between departments
- Forecasting & planning: Design and implementation of company-wide liquidity planning in Power BI – from business logic to a fully automated, data-source-driven planning model replacing the previous manual Excel process; enables rolling forecasts and continuously up-to-date cash flow transparency for management
- Optimization of existing Power BI dashboards in terms of performance, structure, and analytical value using an AI-native approach
- Analysis and improvement of the data model, including data quality analyses, data cleansing, and consistent modeling using star schema, DAX, and Power Query
- Incident & anomaly analysis: Identification, investigation, and explanation of data anomalies, including root-cause analysis and concrete recommendations for action
- AI solution architecture: Connecting Business Central and Power BI to LangDock via MCP (Model Context Protocol) for AI-supported data usage
- Creation of a historical data layer as a basis for trend and time-series analyses
- AI-supported automation: Design and development of AI skills, agents, loops, and processes for the automated analysis and interpretation of reports
- Automated reporting workflow: Setup of scheduled, automated email distribution of AI-generated analyses and recommendations to stakeholders
- Gathering and documentation of business requirements and coordination with business departments and IT as part of requirements engineering / product owner activities
- Breaking down overall requirements into clearly defined work packages and tasks
- Definition, prioritization, and management of milestones throughout the entire project lifecycle
Tools: POWER BI, M365, Copilot Studio, MIRO, Microsoft Business Central, Microsoft Fabric, Claude AI, ChatGPT, LangDock, MS VS Code
Alexander Z.
Last position:
Senior Data Solutions Engineer at VMware Inc.
- Architected and deployed private cloud data platform on VMware vSphere, integrating Greenplum MPP, Apache Kafka, Kubernetes, and Apache Solr, and developed real-time ingestion pipelines with Kafka Connect and Schema Registry.
- Led Oracle Exadata to Greenplum migration, rearchitected data models, optimized storage, implemented RabbitMQ with Debezium for CDC, and deployed VectorDB for Generative AI.
- Designed and executed multi-cloud migration PoC across AWS, Azure, and GCP, defined KPIs for throughput, latency, and cost efficiency, executed bulk data transfers, validated analytics and streaming workloads, and delivered full-scale architecture recommendations.
- Assessed legacy on-premises infrastructure and designed modern cloud-native data platforms using Greenplum and containerized microservices, advising on scalability, disaster recovery, and high-availability.
Haseeb Z.
Last position:
Senior Data Scientist at WPP MEDIA
- Designed and deployed enterprise Retrieval-Augmented Generation (RAG) applications using LangChain, LangGraph, vector databases, embeddings, and open-source LLMs served through vLLM on GCP GPU infrastructure.
- Built agentic AI workflows using LangGraph with planning, reasoning, tool execution, persistent memory, session management, and Human-in-the-Loop approval mechanisms.
- Developed LLM-powered automation systems integrating BigQuery, SQL pipelines, and external advertising APIs including Meta, TikTok, Amazon, Snapchat, Google, and Pinterest, reducing manual operational workflows.
- Architected multi-agent AI systems for enterprise analytics and decision-support workflows, enabling autonomous task execution and intelligent data interactions.
- Implemented retrieval optimization strategies including multi-retriever architectures, semantic search, context optimization, and query improvement techniques, improving response relevance by approximately 40%.
- Engineered structured prompting strategies, function-calling schemas, and validation workflows to improve reliability of multi-step LLM applications.
- Designed scalable AI services using Python, FastAPI, Cloud Run, Pub/Sub, BigQuery, Docker, and cloud-native deployment architectures.
Sejal V.
Last position:
Data & ML Engineering at Consulting
- Fractional leadership; consulting growth-stage startups and scale-ups on data strategy, ML products, and platform foundations
- Building decisioning systems for growth, personalization, & product experimentation, across e-Commerce, Digital Health, Energy, and Logistics
- Exploring Agentic AI & LLM-based tooling for production readiness patterns
Giovanni L.
Last position:
Solution Architect at Nordea Bank
Consumer Cards Solution Architect
- Provided architectural leadership in Consumer Cards Domain establishing best practices and improving architectural transparency and maintainability by designing a structured documentation framework to enable reverse engineering of legacy card systems.
- Standardized architectural artefacts including BIAN Business Capabilities, UML diagrams in draw.io format (Use Case, Component, Sequence), naming conventions, document repository, design templates and blueprints, microservices.
- Produced high-level and low-level designs aligned with enterprise architecture governance processes and artefact standards.
- Provided architectural support to the Strategic Card Simplification Programme, focusing on card product migrations and application decommissioning across all countries. Agile environments (Scrum/SAFe).
- Analysed and designed AI use cases in the architecture domain.
Project: Payment Card Industry Data Security Standards (PCI DSS) Strategic Programme
- Analysed and documented existing data flows across card products and geographic regions to assess PCI DSS compliance.
- Identified areas involving sensitive data at rest and data in motion requiring encryption or masking, ensuring adherence to PCI DSS requirements.
- Collaborated with security, infrastructure, and application teams to align encryption strategies with regulatory and organizational policies.
- Provided strategic advisory services on data strategy, data governance, data management, data quality, data architecture, data mesh, MEGA HOPEX, DAMA-DMBOK, event-driven architecture, end-to-end data flows and card product harmonization models.
- Ensured solution design alignment with regulatory compliance (BCBS 239, DORA, GDPR) and internal policies.
Project: Denmark ATM Outsourcing Project
Objective: Outsource ATM operations and maintenance to a third-party provider while expanding the Denmark ATM fleet, with Nordea retaining ownership of ATMs and cash for the existing and extended infrastructure.
- Led a cross-functional delivery team (project management, business analysis, and architecture) and documented the as-is ATM ecosystem architecture, including end-to-end data flows, integrations, and internal/external application interfaces.
- Designed end-to-end processes for authorization, reconciliation, and settlement, aligning operating model, controls, and compliance requirements across Nordea and the outsourced service provider.
- Produced high-level and low-level solution designs using standardized UML artefacts (Use Case, Component, and Sequence diagrams) to support vendor onboarding, integration planning, and implementation.
- Ensured architectural alignment and decision-making across enterprise stakeholders and third-party providers, managing dependencies and interfaces in the context of the outsourcing initiative.
Wolfram K.
Last position:
AI / Machine Learning Engineer (Projects & Applied AI) at UNIVERSITÉ PARIS 1 PANTHEON-SORBONNE & LIORA
- Designed and implemented a hybrid recommendation system (content-based + collaborative filtering)
- Built end-to-end ML pipelines including data processing, feature engineering, model training, and evaluation
- Developed RAG-based LLM systems using LangChain and vector databases for semantic search and knowledge retrieval
- Established MLOps workflows with MLflow for experiment tracking, versioning, and deployment readiness
- Implemented deep learning models (computer vision & classification) using PyTorch and TensorFlow
Muzamal A.
Last position:
Data Scientist / AI Consultant at HelmX
- Delivered AI and data science solutions, including LLM-based chatbots and data pipelines, improving operational efficiency.
- Collaborated on product features, achieving measurable impact and maintaining strong client relationships.
Tobias L.
Last position:
Data Engineer at unitb consulting GmbH
Tasks: Design and operation of end-to-end cloud data platforms for enterprise clients in publishing and finance, including infrastructure automation, pipeline development, monitoring, and data quality.
Activities:
- Built multi-layer data architectures on Databricks (Apache Spark, Delta Lake), BigQuery, and GCP
- Fully automated cloud infrastructure with Terraform across 3 environments (DEV/STG/PRD)
- Developed automated data pipelines with Python, dbt, and GCP services for different data sources
- Built monitoring and alerting systems for real-time platform monitoring
- Implemented data versioning and quality checks at every layer
- Designed automated test and deployment pipelines in GitLab and Bitbucket
Achievements:
- 2× production data processing capacity, reduced spike response time from minutes to ≤15 s, server errors ≈ 0
- Replaced 3,000 lines of manual configuration with a reusable automation module for 7 customer domains, configuration errors to 0
- Delivered a complete end-to-end data platform at ~€10/month infrastructure cost
- Migrated 7 database tables with 0 downstream issues
- Removed 100% exposed credentials, eliminated external vendor dependency
- Delivered integration of 3 teams in 1 sprint
Diogo S.
Last position:
Backend Engineer and AI Orchestrator at Stealth Startup
- Providing freelance software engineering and AI orchestration services for an early-stage startup.
- Designing and coordinating autonomous AI systems capable of executing complex, multi- step workflows.
- Developing customer-facing pilots and proof-of-concept solutions.
- Participating in meetings with customers and investors to support product development and business discussions.
Hamza K.
Last position:
Academic Research Contributor in Health Sector (Volunteer)
- Acted as technical consultant to optimize multi-layer ensemble models combining ResNet, CNN-BiGRU-Attention, and XGBoost.
- Guided implementation of a Logistic Regression meta-learner to solve class imbalance problems, achieving 92.86% accuracy and 0.9644 AUC on PTB-XL and Chapman-Shaoxing datasets.
Jan K.
Last position:
Data Expert at Manufacturing
Enrico G.
Last position:
Freelance Software & Data/AI Engineer at Freiberuflicher Software & Data/AI Engineer
- Lecturer for the GenAI Track at the Master School Institute of Technology
- Development of a full-stack AI application (React + Python/FastAPI) for automated supplier product import with intelligent column and category classification (4-layer hierarchical) including human-in-the-loop validation
Raphael M.
Last position:
Founder / Quant Developer at Market Maker
- Crypto quant strategy development, automated trade execution, onchain data client (Ethereum / Solana)
- Data and trade architecture development for liquidity provision
Ibrahim H.
Last position:
Senior Full Stack / AI Engineer at Punktum Digital GmbH
- Context: Healthcare and laboratory teams required faster document analysis, treatment-planning support, and reliable AI workflows for MR/VR-assisted operations.
- Contribution: Built the AI healthcare platform, model/agent workflows, VR-glasses deployment platform, REST APIs, Next.js/React interfaces, and CI/CD pipelines.
- Impact: Delivered a production-ready AI product foundation that improved clinical document review, supported laboratory automation, and made VR fleet deployment manageable across environments.
Tech: TypeScript, Next.js, Node.js, React, Java, Spring Boot, Python, PyTorch, TensorFlow, Docker, PostgreSQL, OpenAPI, GitLab, GitHub Actions.
Nikunjkumar P.
Last position:
Senior Java Backend Developer at Questax Professionals GmbH
- Provide the Price Listing Service team with prices for all cars and vans in different markets
- Adapt market-specific requirements such as taxes, government subsidies, campaigns
- Import and synchronize new prices for all new and existing cars and store them in the Redis datastore
- Support our product and on-call duty
- Daily business, development of new features, bug fixing
- Pair programming, code review, mob programming
- Develop POCs for new ideas
- Maintain and extend the backend
- DevOps tasks
- Run Kubernetes updates
- Adjust and further develop Kubernetes resources
- Develop and adapt Helm charts
- Adapt and update ArgoCD
- Maintain, adapt, and improve CI/CD
Discover over 15,000 top freelancers
Statistics of experts using Apache Spark
Aggregated from the professional profiles of matched freelancers.
Experience
13 years (Germany: 14 years)
Position duration
2.1 years (Germany: 2.7 years)
Positions per freelancer
8 (Germany: 10)
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Banking and Finance, Automotive
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
97%
Master's degree or higher
63% (Germany: 71%)
Doctorate
9% (Germany: 13%)
Certifications per freelancer
3
Most common languages
English, German, French
Speak two or more languages
89% (Germany: 97%)
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 Berlin 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 Berlin 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 (92%)
- Banking and Finance (43%)
- Automotive (38%)
- Professional Services (38%)
- Education (35%)
- Retail (30%)
- Energy (27%)
- Healthcare (27%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Apache Spark does
Apache Spark is an open-source engine for distributed data processing. It runs large-scale batch jobs, interactive analytics, machine learning workloads, and streaming pipelines across clusters. Teams use it when data volume, velocity, or transformation complexity exceeds the practical limits of a single machine.
Core processing model
Spark distributes computation across executors coordinated by a driver. Its DataFrame and Dataset APIs support optimized transformations through Spark SQL, while resilient distributed datasets remain useful for lower-level control. Strong specialists understand partitioning, shuffles, caching, serialization, and fault recovery rather than treating Spark as a black box.
Ecosystem and tooling
Apache Spark connects with many storage systems, message brokers, and cloud services. Relevant skills often include:
- Spark SQL, DataFrames, Datasets, and Structured Streaming
- Delta Lake, Apache Iceberg, or Apache Hudi data tables
- Apache Kafka, Hadoop Distributed File System, and object storage
- PySpark, Scala, Java, cluster managers, and workflow orchestration
Where companies use it
Companies use Spark for lakehouse transformations, customer and product analytics, log processing, fraud detection, recommendation features, and operational reporting. It also supports feature preparation and model pipelines when workloads need distributed computation. In Berlin, specialists may work with teams in mobility, finance, retail, media, or industrial technology, remotely or alongside local teams.
When outside expertise helps
Freelance expertise is useful when a pipeline is slow, unstable, expensive to operate, or difficult to maintain. Bring in a specialist when you need to:
- Move legacy Hadoop or SQL workloads into a modern lakehouse
- Design reliable streaming with event-time handling and checkpointing
- Diagnose skew, excessive shuffles, memory pressure, or failed jobs
- Establish testing, deployment, observability, and data quality practices
What strong specialists deliver
A strong Apache Spark professional links application logic to cluster behavior and business outcomes. They define clear schemas, idempotent transformations, sensible partition strategies, and observable pipelines. They document trade-offs, test data edge cases, and can explain why a design should use Spark rather than a warehouse query, a stream processor, or a simpler service.
Frequently asked questions
Not sure where to start with Apache Spark? These answers cover the essentials.
Apache Spark is used to process and transform large datasets across distributed machines. Companies use it for batch analytics, streaming, data lakehouse pipelines, machine learning preparation, and complex SQL workloads.
Apache Spark covers batch processing, SQL, streaming, and machine learning in one broad ecosystem. Apache Flink can be a stronger fit for stateful, low-latency streaming, while a cloud data warehouse may be simpler for managed analytical SQL; the right choice depends on latency, control, data location, and operating needs.
A strong Apache Spark specialist often works with Python, Scala, or Java and understands SQL, Kafka, Kubernetes, and cloud storage. Experience with Delta Lake, Apache Iceberg, orchestration, data quality, and observability is also valuable for production delivery.
An Apache Spark freelancer should show relevant production work, not only notebooks or classroom exercises. Look for experience with pipeline ownership, cluster behavior, failed-job diagnosis, schema changes, deployment, and measurable data reliability improvements.
Apache Spark projects usually work well remotely because code, pipelines, cloud environments, and monitoring can be reviewed collaboratively. For Berlin-based teams, agree early on working hours, documentation standards, communication language, and whether occasional on-site workshops are needed.
Review whether Apache Spark solutions are correct, repeatable, observable, and efficient under realistic data volume and failure conditions. Ask the specialist to explain partitioning, shuffle behavior, checkpointing, schema evolution, testing, and the operational trade-offs behind the design.
PySpark is often enough for data transformation, analytics, and many machine learning pipelines. A project may also need Scala or Java knowledge when performance-sensitive libraries, custom Spark extensions, or deeper integration with the JVM ecosystem are involved.
Choose Apache Spark when distributed processing, varied data sources, streaming, or complex transformations justify its operational model. A warehouse query, managed ETL service, or lightweight Python process may be better for smaller, stable workloads with limited pipeline complexity.
The average hourly rate of freelancers in Berlin, Germany who have used Apache Spark in their recent projects is 86 €, which corresponds to a daily rate of about 687 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used Apache Spark in their recent projects, 97% hold at least a Bachelor's degree, 63% hold at least a Master's degree, and 9% hold a doctorate.
On average, freelancers in Berlin, Germany who have used Apache Spark in their recent projects have 13 years of professional experience, with a single engagement typically lasting around 2.1 years.
The most common languages among freelancers in Berlin, Germany who have used Apache Spark in their recent projects are English (97%), German (89%), and French (14%).
The most common industries among freelancers in Berlin, Germany who have used Apache Spark in their recent projects are Information Technology (92%), Banking and Finance (43%), and Automotive (38%).
The most common business areas among freelancers in Berlin, Germany who have used Apache Spark in their recent projects are Information Technology (100%), Business Intelligence (89%), and Product Development (89%).
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.
Hamburg 
Munich 
Cologne 
Frankfurt 
Nuremberg Request a free demo
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