Big Data Experts
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Meet FRATCH Experts who have recently used Big Data
Dany-Armand D.
Last position:
Senior Data Scientist at ibg NDT GmbH
- Investigate the relationship between Eddy Current Testing (ECT) signals and microstructural properties
- Detect latent patterns in ECT data that reflect intrinsic material characteristics
- Develop and validate predictive models for microstructural classification and quantification, using hardness and case depth as benchmarks
- Apply Bayesian Structural Equation Modeling for advanced data analysis
Umut G.
Last position:
Data Architect at BA Technology
I am an experienced data engineer specializing in end‑to‑end data integration, cloud DWH architectures, and high‑quality, governed data products.
I delivered following projects and engagements as a freelancer.
- Data Migration of CRM System for AL-FA Objekt Service Gmbh
- Microsoft Software Resales Partnership
I am looking for freelance roles like: Freelance Data Engineer Cloud Data Warehouse Architect Data Modeling & Architecture Consultant MDM & Data Governance Specialist BI & Analytics Developer
Technical Focus Areas
- Data Engineering & Integration: SQL Server/SSIS, Informatica PowerCenter/IDQ, Talend, Kafka, Azure Data Factory – Delta/CDC/ELT patterns, robust pipelines, monitoring/recovery, data lineage & impact analysis, medallion architecture Bronze/Silver/Gold layers
- DWH & Cloud: Azure SQL / Data Lake / Synapse, AWS Redshift/S3, on‑prem SQL/Oracle – scalable data marts with a strong cost/benefit focus.
- Data Modeling: Atomic (Inmon) and Dimensional (Kimball), Data Vault (Linstedt), Domain‑Driven Design, clear lineage & contracts.
- MDM & Governance: Informatica MDM, IBM MDM, stewardship processes, data quality rules, survivorship/XREF, catalog/glossary, SIF/BES/REST publication.
- Analytics/BI: Power BI, SSAS, Cognos – business‑ready, maintainable data products.
Hachem S.
Last position:
Machine Learning & BI Lecturer at EU Business School
- Taught ML, BI, and Big Data to Master's students.
Tech: Python, Power BI, SQL, ML, SnowflakeDB, databricks, MS Fabric, MongoDB, LLM
Alexander H.
Last position:
Senior Product & Strategy Consultant at hegehofer.consulting
Design, development, and market launch of digital products and platforms – from strategic alignment and agile implementation to Go-to-Market and Go-Live, integrating AI into processes and products
Stefan O.
Last position:
Founder at ProtocolEngine.io
Evidence-led health intelligence platform turning published research into personal health protocols. It scores 430 habits, foods, and supplements against the studies behind them, and moves the score when the evidence moves. Built solo.
- Built the daily ingestion pipeline across PubMed, bioRxiv, and medRxiv: 43,000+ papers from 3,400+ journals processed into 230,000+ typed evidence claims, each one traceable back to the study it came from.
- Designed the six-factor evidence scoring model and the public changelog behind it, so no recommendation ever appears without the papers underneath it. 23,000+ grade changes recorded and explained to date.
- Shipped an entity information model connecting every intervention to its mechanisms, biomarkers, and outcomes: 118 biomarkers with region-specific reference ranges, 77 mechanisms, 32 graded outcomes.
- Built the personalisation layer: blood panel ingestion that reads lab PDFs with a vision model and corrects results for draw time against the user's wake anchor, plus Oura, WHOOP, and Withings integration for daily readiness context.
- Operate eleven specialised review agents over the corpus and codebase, covering paper curation, retrieval quality, health-claim compliance across EU and US regimes, and security.
- Shipped the Evidence Assistant, a RAG assistant that answers from the claim database and cites the underlying papers, plus a B2B practitioner tier, an Expo React Native app, and localisation across 3 languages and 7 markets.
Stack: Next.js 16, TypeScript, Supabase, pgvector, Anthropic Claude, Vercel, DeepInfra.
Vladimir F.
Last position:
IT Consultant at ITZBund
- ITSM consulting, analysis, and optimization of IT capacity using BMC TSCO / BMC Helix
- Developed the concept for “Governance Guidelines for Operational Capacity Management”
Kiriakos K.
Last position:
Tech Lead / Architect : OTTO API Platform at OTTO
Maturing their API practices on both a business and technology level. My role covers strategy, architecture, developer advocacy as well as hands-on software engineering, enabling both technical teams and business leadership to adopt and act on API-centric principles effectively. Coincidentally, we also establish GitOps, DX and platform best practices with this project.
Highlights:
- Aligning executives with the initiative by clarifying strategy, replacing misconceptions and myths with facts, clarifying the value of existing assets and enabling informed decision-making
- Formulating a way forward for API Lifecycle Management at OTTO
- Driving platform progress and fostering developer engagement by hands-on engineering work towards strategic goals
API Lifecycle Management, Team Topologies, Organizational Evolution, Regulatory, Platform Advocate, Developer Platform, Communities of Practice, Terraform, Kotlin, Kafka, Kong, WSO2, Apigee, Gravitee, Backstage, AsyncAPI, OpenAPI, API Design, AWS, React, Node.js, TypeScript, Redocly, reactive programming, CDC, Golang, Gin, GitOps, DX (developer experience), stakeholder management, roadmaps, workshops, discovery.
Jens H.
Last position:
Interim CTO (occasional assignments) at Fujitsu / FSAS
Stabilization of an Azure/.NET landscape in live operation.
- Architecture, DevOps, and operational readiness; technical decisions under time pressure
- Azure DevOps, monitoring, ETL/ELT, cloud security, FinOps, and data-mesh-related topics
Technologies: Azure DevOps, .NET, CI/CD, monitoring, FinOps
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.
Olga L.
Last position:
Business Analyst at VisualVest (Union Investment)
- Analyzed, structured, and documented business requirements for digital investment solutions, such as robo-advisors.
- Designed applications for new retirement products, including user flows, UX requirements, and functional specifications.
- Modeled and optimized business processes and coordinated with stakeholders while taking regulatory requirements in the financial sector into account.
Will O.
Last position:
Solution Architect at Polizei Behörde Stuttgart
Designed a migration strategy for WLS applications to BKA Cloud and Stack IT
Created a hybrid cloud for internal testing and development with Kubernetes
Transformed the IT landscape from an outdated monolithic architecture to a cloud-based architecture
Integrated new CI/CD processes using Azure Pipelines
Quadrupled the processing throughput for personnel security checks
Migrated personnel security screening applications to Docker-based applications
Implemented CI/deployment pipelines using Azure DevOps
Environment: Java Springboot, Kubernetes, Oracle Weblogic, OpenID, Camunda, JUnit, TOGAF
Prasad T.
Last position:
Solution Architect / Senior Manager – DTC E-Commerce Platform at BRITA
- Led discovery phase and POC for Shopware to Shopify Plus migration across EMEA markets, evaluating platform suitability, technical architecture, and multi-brand/multi-country capabilities against business requirements.
- Designed reference architecture for Shopify Plus implementation incorporating headless front-end patterns (Vue.js, Nuxt.js), CMS integration (Magnolia), and Azure middleware (APIM, Functions, Logic Apps, Service Bus) for 11 EMEA markets.
- Defined migration strategy analyzing data mapping, cutover approach, and zero-downtime deployment patterns using Varnish caching, GitOps pipelines, and CI/CD orchestration across six vendor teams.
- Architected multi-tenant Shopify Plus governance model with centralized admin, localized storefront customization, and compliance controls (GDPR, data residency).
- Prototyped AI-driven search optimization (LLM.txt, JSON-LD) for product discoverability in Google AI results, demonstrating post-launch performance opportunities.
- Defined EMEA expansion roadmap for 15+ markets through C-level strategic workshops, identifying phased rollout, market-specific configurations, and resource requirements.
- Tech Stack: React, Nuxt.js, Vue.js, Magnolia CMS, Shopware, Shopify Plus, Azure (APIM, Functions, Logic Apps, Service Bus, Front Door), Varnish, SAP, MS Dynamics, Docker, Kubernetes, GitHub Actions, PostgreSQL, Kafka
Jan E.
Last position:
Participant in Project Management Certifications at Business Trends Academy BTA GmbH
Certifications earned:
- EXIN Agile Scrum Master
- EXIN Agile Product Owner
- GPM Basiszertifikat im Projektmanagement
- IPMA Level D
David K.
Last position:
Head of Enterprise Architecture Management at Rödl & Partner
- Worked closely with senior management to further develop the company's technology vision and strategy
- Helped shape the product portfolio by implementing innovative technologies and optimizing existing IT architectures
- Technical Lead – Center of Excellence Intelligent Automation (especially AI)
- Global Competence Center Lead – Oversaw technical delivery and implementations
- Program Lead – Managed key topics during implementation
- AI Architect & Solution Architect – Supported and advised projects on requirements, solutions, and architecture (technical and content-related)
- Data Strategy & Data Stack Definition – Established a company-wide data strategy, including an implementation plan and the evaluation, piloting, and selection of a data stack
Aurelia H.
Last position:
Software Full Stack Developer at INNIO Jenbacher GmbH & Co OG
- Implementing and extending an Electron app based on TypeScript, React and Node.js to display stream content and technical data
- Extending a Kotlin backend for message processing via Kafka
- Extending various React modules
- Programming custom solutions for customers, including testing
- Creating documentation
- Development in accordance with Scrum processes
Used skills: React, React Hooks, Jest, Vitest, Typescript, Electron, IntelliJ, Vite, Github, Jira, Node.js, yarn, npm, pnpm, Swagger, OpenAPI, Storybook, Docker, Prettier, Eslint, tailwindcss, i18n, GraphQL, CoPilot, Postgres, JSON, Sqlite, trpc, influxdb, Java, Spring, Kotlin, Kafka, Git, Sonarcube
Discover over 15,000 top freelancers
Statistics of experts using Big Data
Aggregated from the professional profiles of matched freelancers.
Experience
19 years
Position duration
3 years
Positions per freelancer
12
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Professional Services, Banking and Finance
Certification focus areas
Information Technology, Project Management, Business Intelligence
Bachelor's degree or higher
93%
Master's degree or higher
65%
Doctorate
16%
Certifications per freelancer
3
Most common languages
German, English, French
Speak two or more languages
98%
Based on our profile pool as of 5 Nov 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology 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 using Big Data
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 5 Nov 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Big Data 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 (85%)
- Professional Services (48%)
- Banking and Finance (46%)
- Manufacturing (44%)
- Automotive (37%)
- Education (35%)
- Healthcare (33%)
- Retail (33%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Big Data covers
Big Data refers to methods and systems for collecting, storing, processing and analysing data that is too large, fast-moving or varied for conventional tools. It supports batch and real-time workloads, from event streams and customer records to sensor data and machine logs. The goal is usable insight, dependable operations and informed decisions.
Typical use cases
Companies use Big Data to build data platforms that combine information from many sources and make it available for reporting, analysis and automated processes.
- Process clickstream, transaction and application data
- Create recommendation, fraud detection and forecasting systems
- Analyse IoT, telecoms, logistics and industrial data
- Support machine learning with prepared, governed datasets
Ecosystem and tooling
The ecosystem includes distributed storage, stream processing, orchestration and analytical query tools. Hadoop remains relevant for distributed data storage and batch processing, while Apache Spark supports large-scale transformation, SQL, streaming and machine learning workloads. Kafka, Flink, Hive, Trino, Airflow and cloud services often connect the wider platform.
When specialists help
Freelance specialists are valuable when an organisation is replacing fragmented data workflows, moving workloads to the cloud or launching a new analytics product. They can assess source systems, choose suitable processing patterns and create pipelines that remain observable and maintainable. External expertise also helps when internal teams need focused support during a migration or delivery phase.
- Connect operational systems, APIs, files and event streams
- Improve pipeline reliability, latency and data quality
- Establish governance, access controls and lineage
Skills that matter
Strong professionals understand distributed computing, data modelling, SQL and programming with Python, Java or Scala. They know how to manage schemas, partitions, storage formats, resource usage and failures across a cluster or cloud environment. They also connect technical choices to reporting needs, privacy requirements and measurable business outcomes.
How to assess quality
Look for evidence of complete delivery rather than familiarity with isolated tools. A capable Big Data specialist can explain ingestion, transformation, storage, orchestration, monitoring and recovery as one system. Ask how they handle changing schemas, late events, duplicate records, access permissions and rising workloads. Clear documentation, meaningful tests and transparent trade-offs are strong signs of quality.
Frequently asked questions
Need clarity? These are the questions we hear most often about Big Data.
Big Data is used to collect and process large, fast-changing or varied datasets. Companies apply it to customer analytics, fraud detection, recommendations, forecasting, operational monitoring and machine learning.
Big Data systems are designed for distributed processing across varied data sources and workload types. A traditional warehouse is often more structured and focused on governed analytical queries, while modern projects may combine both approaches.
A strong Big Data specialist may work with Apache Spark, Hadoop, Kafka, Flink, Hive, Trino and Airflow, alongside cloud storage and analytical services. The right combination depends on data volume, latency, governance and existing systems.
Professionals working with Big Data often need SQL, Python, Java or Scala, data modelling, cloud infrastructure and orchestration skills. Knowledge of machine learning, data governance, observability and security is also useful when the platform supports production decisions.
The needed background depends on the project scope, data complexity and operational risk. A focused pipeline improvement may suit a specialist with a narrower remit, while a distributed platform migration calls for someone who has handled architecture, reliability and delivery across the full lifecycle.
Most Big Data work can be delivered remotely because systems, documentation, repositories and cloud environments are accessed online. On-site collaboration can still help during discovery, workshops or work involving restricted infrastructure, and shared language expectations should be agreed early.
Ask the Big Data professional to explain how they would manage ingestion, schema changes, failures, monitoring, permissions and recovery. Strong answers connect technical decisions to data quality, cost control, business requirements and long-term maintainability.
Big Data may be unnecessary when datasets are modest, workloads are simple and a conventional relational database or warehouse meets the need. A good specialist will test that assumption instead of adding distributed infrastructure without a clear operational benefit.
The average hourly rate of freelancers who have used Big Data in their recent projects is 103 €, which corresponds to a daily rate of about 827 € based on an 8-hour working day.
Of the freelancers who have used Big Data in their recent projects, 93% hold at least a Bachelor's degree, 65% hold at least a Master's degree, and 16% hold a doctorate.
On average, freelancers who have used Big Data in their recent projects have 19 years of professional experience, with a single engagement typically lasting around 3 years.
The most common languages among freelancers who have used Big Data in their recent projects are German (98%), English (98%), and French (22%).
The most common industries among freelancers who have used Big Data in their recent projects are Information Technology (85%), Professional Services (48%), and Banking and Finance (46%).
The most common business areas among freelancers who have used Big Data in their recent projects are Information Technology (95%), Business Intelligence (75%), and Product Development (74%).
Main locations of FRATCH Experts, who have recently used Big Data
Our freelancers and interim experts are at home all over Germany — available on-site in Berlin, Hamburg, Munich and every major business hub, or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.
Berlin 
Hamburg 
Munich 
Cologne 
Frankfurt 
Stuttgart 
Vienna Across Switzerland our specialists are active in Zurich, Geneva, Basel and Bern — working on-site or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.
Zurich Geneva Basel Bern Countries:
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FRATCH CEO
