Apache Spark Experts in Berlin
for scalable data processing, matched in minutes with vetted and available freelancersHire experts who build batch and streaming pipelines, optimize distributed workloads, and connect Spark with modern data lakes and cloud services. FRATCH finds the right vetted, available freelancer through fast, precise AI matching.
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 using a unified data model as the company-wide single source of truth
- Consolidation of previously isolated reporting logic in a central semantic model – elimination of redundant files, manual data transfers and inconsistent metrics across 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; enabling 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 specific recommendations for action
- AI Solution Architecture: Connecting Business Central and Power BI to LangDock via MCP (Model Context Protocol) for AI-supported data usage
- Development 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 delivery 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
Vili D.
Last position:
Technical Lead, Data Engineer at Mercedes-Benz Consulting
Optimized the data architecture (Medallion) to better decouple processing steps and improve transparency and reproducibility
Ensured the technical quality of data processing in Databricks by introducing schema enforcement, data quality checks, and a structured data architecture
Pipeline orchestration with Azure Data Factory
Improved and automated the development and deployment process by integrating Git and GitHub Actions
Technical leadership of the Data Engineering team (3 employees)
Led workshops to optimize and stabilize the data platform and development process
Collected and prioritized new requests and maintained the product backlog
Technologies: Microsoft Azure (Data Lake, Data Factory), Databricks, Apache Spark (PySpark), Python, SQL, Git, Confluence, Power BI, Power Apps, Dataverse, MS Sharepoint, Mural
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.
Gyan P.
Last position:
Senior DevOps & Cloud Architect, Head of Infrastructure at Bosch
- Azure Landing Zone architecture: Design and implement a hub-spoke, multi-region-capable Azure landing zone that provides the secure, scalable and highly available foundation for autonomous driving and parking data/ML platforms.
- Compliance through Azure Policy: Enforce security and compliance standards across Azure subscriptions via Azure Policy (guardrails, deny/audit/deploy-if-not-exists), consistently achieving top-tier infrastructure security scores.
- Infrastructure-as-Code: Automate provisioning and configuration of Azure resources with Terraform and Bicep, with changes delivered through reviewed CI/CD pipelines.
- Azure Databricks and Unity Catalog: Extensive experience operating Azure Databricks with Unity Catalog for centralized data governance, access control, lineage and auditing across workspaces.
- Monitoring and observability: Built and run infrastructure monitoring and logging with Azure Monitor, Prometheus, Grafana and Loki, covering platform health, alerting, troubleshooting and capacity trends.
- Platform at scale: Architected and operate cloud data/ML platforms supporting multi-PB vehicle data ingestion and simulation; orchestrate Argo Workflow-based recompute and autolabeling services scaling to 100k+ concurrent pods on AKS.
- Reproducible ML: Established reproducible training environments with dataset versioning and provenance tracking, enabling auditing and experiment reliability.
- Operational excellence: Automated GitHub organization management and CI/CD pipelines, improving developer productivity and deployment reliability; automated recurring operational processes.
- FinOps: Developed transparent resource utilization and cost-tracking for multi-project Azure environments.
- Leadership: Technical lead for infrastructure topics, collaborating with development, DevOps and security teams; technical interviewer and recruitment panel member for scaling the engineering team.
Hamza K.
Last position:
Academic Research Contributor in Health Sector (Volunteer) at n/a
- 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.
Mahboobeh S.
Last position:
Senior Analytics Engineer at Jadoosoft
Engaged as an independent BI consultant for a FinTech SaaS platform, building and maintaining the end-to-end Microsoft Azure and Fabric analytics stack behind a multi-tenant accounting product used by business owners to track their own financial performance.
- Partnered with finance stakeholders to translate complex accounting workflows into real-time BI dashboards (P&L, Cash Flow, Balance Sheet), helping 2000+ business owners track cash flow and overall financial health effortlessly.
- Developed 10+ interactive dashboards, giving each business owner personalized, real-time visibility into their own sales performance, accounts receivable aging, and operational costs.
- Enforced strict enterprise data governance by implementing dynamic Row-Level Security (RLS) via DAX to isolate each business owner's data within the shared multi-tenant model.
- Maintained and extended an Azure ELT pipeline (Azure Data Factory, Azure SQL Database, Azure Synapse) that automated data ingestion and transformation.
- Contributed to a team-wide platform migration from Azure to Microsoft Fabric to optimize capacity licensing costs.
- Designed and implemented a Microsoft Fabric ELT pipeline (Lakehouse, Pipelines, PySpark) that automated data ingestion and transformation, replacing the prior Azure pipeline.
- Modeled cleansed data into a Star Schema within the data warehouse using T-SQL, powering the Power BI Semantic Model.
- Leveraged AI tools, including Copilot and Claude, as development partners across the entire ELT pipeline, from data ingestion to KPI reporting and visualization.
- Supported agile delivery processes, using Jira to track development tasks and Confluence to maintain technical and project documentation.
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
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.
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
Jan K.
Last position:
Data Expert at Manufacturing
Tobias L.
Last position:
Data Engineer at unitb consulting GmbH
Responsibilities: 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 various data sources
- Built monitoring and alerting systems for real-time platform monitoring
- Implemented data versioning and quality checks at every layer
- Designed automated testing and deployment pipelines in GitLab and Bitbucket
Achievements:
- Doubled 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, reducing configuration errors to 0
- Delivered a complete end-to-end data platform at ~10 €/month in infrastructure costs
- Migrated 7 database tables with 0 downstream issues
- Removed 100 % of exposed credentials, eliminating dependency on external vendors
- Delivered 3-team integration in 1 sprint
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
2.2 years (Germany: 2.7 years)
Positions per freelancer
7 (Germany: 10)
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Banking and Finance, Professional Services
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
97%
Master's degree or higher
65% (Germany: 70%)
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 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Berlin 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 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 29 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.
- Information Technology (92%)
- Banking and Finance (45%)
- Professional Services (39%)
- Automotive (34%)
- Education (34%)
- Retail (32%)
- Manufacturing (29%)
- Energy (26%)
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. Teams use it for batch analytics, real-time streaming, machine learning workflows, and data transformation. Its in-memory execution and parallel processing help handle workloads that exceed a single machine.
Core APIs
Spark supports Scala, Python, Java, and SQL through APIs for DataFrames, Datasets, and resilient distributed datasets. Strong specialists choose the right abstraction, manage schemas, and design transformations that remain readable and efficient. PySpark is common when Spark work must fit into broader Python data workflows.
Ecosystem and Tooling
Spark often sits within a wider data platform rather than operating alone. Relevant expertise includes:
- Spark SQL for structured queries and analytical workloads
- Structured Streaming for continuous data pipelines
- MLlib for distributed machine learning tasks
- Delta Lake, Iceberg, or Hudi for lakehouse storage
- Kubernetes, YARN, or managed cloud services for cluster operations
Where It Fits
Companies bring in Spark specialists for event pipelines, customer analytics, recommendation systems, fraud detection, log processing, and large-scale reporting. In Berlin, this can include data-intensive work across technology, finance, mobility, retail, and research. Remote delivery works well when data access, environments, and ownership are clearly arranged.
When to Bring in Expertise
Freelance support is useful when jobs run slowly, cloud processing costs rise, or a team needs to move from scripts to reliable pipelines. Typical signs include repeated driver failures, excessive shuffles, poor partitioning, unclear schemas, and streaming jobs that cannot recover cleanly. Specialists can review architecture, tune workloads, and document the resulting system.
What Strong Specialists Deliver
Good Spark professionals connect code decisions to data architecture and business requirements. They understand joins, partitioning, caching, serialization, checkpointing, and fault tolerance, while also testing data quality and operational behavior. Look for clear delivery around pipeline design, performance evidence, deployment, monitoring, and handover to the internal team.
Frequently asked questions
Not sure where to start with Apache Spark? These answers cover the essentials.
Apache Spark is used to process and analyze large datasets across multiple machines. Companies use it for batch transformation, streaming analytics, machine learning pipelines, reporting, and data preparation for warehouses or lakehouses.
Apache Spark usually offers a more flexible programming model and better performance for iterative workloads because it can keep intermediate data in memory. Hadoop MapReduce remains relevant for some simple batch jobs, but Spark provides richer APIs for SQL, streaming, and machine learning.
Apache Spark supports all three choices, and the right option depends on the team and workload. Python is practical for data science and PySpark pipelines, Scala offers close integration with Spark internals, and SQL works well for structured transformations and analytics.
A strong Apache Spark specialist often also understands cloud storage, data lake formats, Kafka, orchestration tools, and cluster deployment. Experience with Kubernetes, Databricks, Delta Lake, Iceberg, or warehouse integration can be valuable when Spark is part of a larger platform.
The required depth depends on the workload rather than the technology name alone. A simple transformation may need solid PySpark and SQL knowledge, while streaming, performance tuning, security, and production operations call for a specialist who has handled distributed failure modes and data quality issues.
Apache Spark projects are often suitable for remote collaboration because code, cloud environments, and pipeline documentation can be shared securely. On-site work in Berlin may help when access controls, hardware, domain workshops, or close coordination with a local data team are central to delivery.
Ask for a clear explanation of partitioning, joins, shuffle behavior, recovery, and expected resource use. High-quality Apache Spark work includes tested transformations, observable pipelines, sensible cluster settings, data validation, and documentation that lets the team operate and extend the system.
Apache Spark may be unnecessary for small datasets, simple scheduled queries, or low-latency workloads that need a specialized serving system. A lightweight SQL tool, a warehouse-native transformation layer, Flink, or a purpose-built database may be a better fit when distributed batch processing is not the main requirement.
The average hourly rate of freelancers in Berlin, Germany who have used Apache Spark in their recent projects is 90 €, which corresponds to a daily rate of about 722 € 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, 65% 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 2.2 years.
The most common languages among freelancers in Berlin, Germany who have used Apache Spark in their recent projects are English (97%), German (92%), and French (13%).
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 (45%), and Professional Services (39%).
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 (87%).
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 
Dusseldorf 
Nuremberg Countries:
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