MongoDB Experts in Berlin
matched in minutes from over 15,000 CVs with the power of AIHire experts who design document models, build resilient data services and connect MongoDB with cloud-native applications, analytics systems and APIs. FRATCH finds the right vetted, available freelancer through fast, precise AI matching.
Meet FRATCH Experts in Berlin, who have recently used MongoDB
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
Sascha B.
Last position:
Web Developer at GxPlex
- Built a customized MediaWiki instance, including installation, MySQL database, SSL, and automatic backups
- Set up user roles (Admin, Mod, Verified, User) and a permissions system
- FlaggedRevisions for editorial review workflows · Commenting and rating extensions
Chintan P.
Last position:
Product Owner and Technical Product Lead at Sustamize GmbH
LLM-based features for automated CO₂e data extraction from unstructured documents (70% reduction)
Agentic AI pipeline for automated Scope 3 emissions calculations with 150.000+ validated data records
Intelligent API workflows for real-time carbon footprint calculations in ERP and ESG systems
ML algorithms for predicting emissions hotspots and optimizing product design
Automated data validation pipelines with NLP for quality assurance of CO₂e datasets
Led a 15-person cross-functional team in developing 10+ AI features
Strategic product planning and AI roadmap with 35% shorter time-to-market
Stakeholder management with DAX companies (40% higher satisfaction, 95% retention)
On-time project delivery with 95% budget adherence through data-driven backlog management
Agile methods (Scrum, Kanban) with continuous AI/ML integration (25% increase in team velocity)
Product-market fit for AI features through A/B testing and analytics (60% higher adoption rate)
Oleg A.
Last position:
Staff Software Engineer at Kpler Germany GmbH
- Delivered a new notifications platform implementation built from scratch to replace existing and upcoming services
- Collaborating with other teams to integrate more domains
Tech stack:
- Data: Scala 3, Apache Kafka, Python, Airflow, Astronomer
- BE-FE: TypeScript, NestJS, Java, Spring Boot, Vue
- Dev-ops: AWS, PostgreSQL, Docker, GitHub Actions, Kubernetes, Helm, ArgoCD
Imran A.
Last position:
Software Engineer II at LivePerson Germany GmbH
- Led development of 15+ microservices (Java 17, Spring Boot) driving customer interactions; migrated from on-prem to GCP Kubernetes, improving scalability and reducing infra cost by 20%.
- Optimized user services with CouchDB caching and API refactoring, cutting response times by 35% and enhancing customer experience.
- Implemented canary deployments, FluxCD GitOps, and CI/CD optimizations in GitLab, reducing release lead time by 25% and enabling zero-downtime rollouts.
- Set up Grafana health checks and Anodot alerts for latency, error, and throughput monitoring, reducing MTTR by 40%.
- Built secure APIs using OAuth2, DPoP, and Gatekeeper, integrated REST and GraphQL, and achieved 90%+ test coverage with unit and E2E tests.
- Mentored junior developers, promoted Agile best practices, and collaborated cross-functionally to deliver high-impact, reliable customer-facing features.
Saman S.
Last position:
AI Product Builder at Instalemon.com
- Architected and built an agentic creative automation platform on Mastra, with a custom RAG pipeline, custom hooks, tools and skills, Chroma for vector storage, and a MongoDB/Express backend.
- Built the agent orchestration layer powering Pixomi's multi-agent workspace, including 72 custom marketing skills, tools and hooks, and a custom context-management pipeline.
- Designed and implemented evals and observability through Mastra studio.
- Onboarded 10 pilot SMB customers producing 10x publish-ready creative output per campaign versus manual production in 3 months.
- Ran customer discovery and pilot feedback loops to shape the roadmap for an AI-native, workflow-based creation platform.
Nada S.
Last position:
Freelance Senior Frontend Engineer at Self-Employed
- Senior software engineer focused on React, TypeScript and AI-assisted product workflows
- Build frontend systems for complex SaaS products, internal tools and operational workflows
- Recent work includes AI evaluation tooling, support reliability analysis and developer-focused QA systems
- Available for freelance and contract engagements, especially remote-first projects
Rüdiger S.
Last position:
Full-Stack Software Engineer / Consultant for Digitalization at ARTEVENT
Designed, built, and launched an internal event planning web application used by over 100 department leads for a large event, despite having no dedicated testing phase.
Ensured smooth, failure-free operation during first production use, leading to the tool being adopted for future events.
Automated catering calculations and related workflows, significantly reducing email communication and manual computation effort for meal planning.
Managed deployment and hosting on a Linux server using Coolify, including application setup and runtime operations.
Hired and guided a communication designer on UX while independently owning all technical decisions and implementation.
Jorge P.
Last position:
Software Engineer – AWS and Kubernetes Specialist at Citti
- Creation, maintenance and hardening of Kubernetes clusters employing Ansible and ArgoCD
- Keywords: Ansible, AWX, Kubernetes, NetApp, Prometheus, CI/CD ArgoCD, SSO, Fluent-bit, HAProxy, Calico, Keycloak, oauth2-proxy, SealedSecrets, kubeseal, Aqua kube-bench, CIS-Benchmarks, Aqua Trivy operator
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.
Aruldass A.
Last position:
Web Module Lead at Mphasis Limited
- Led the end-to-end delivery of enterprise full-stack web applications by driving requirement analysis, solution design, frontend and backend development, database design, API integration, code reviews, team coordination, Agile execution, CI/CD deployments, production support, performance optimization, security implementation, and stakeholder collaboration to deliver scalable, high-quality software solutions.
Abhiroop B.
Last position:
Software Engineer III at Foundry Digital
- Developed and deployed microservices in Kotlin and Spring Boot, integrated AWS Secrets Manager to secure credentials and decreased network calls using Spring cache.
- Refactored Kafka consumer using Spring Kafka with semaphore-based backpressure to cap records and keep heap memory stable under spikes; switched to batch upserts to cut down on database invocations; added Testcontainers integration tests for Kafka and database to pave the way for future changes.
- Automated the financial reconciliation workflow in Spring Boot (Kotlin) using Spring Scheduler, transactional boundaries, JPA/Hibernate on MySQL, and Flyway migrations, saving the accounts team 16+ hours per week.
- Designed and dockerized payments end-to-end test framework in Robot (Python) with reusable keyword libraries and profiles; integrated with GitLab CI (JaCoCo XML and HTML reports) to accelerate releases and lift code coverage to 80%.
- Implemented end-to-end observability on Datadog by instrumenting services with Datadog APM, correlating metrics and logs, provisioning dashboards, and creating monitors with burn-rate alerts and anomalies to harden reliability and give stakeholders clear visibility.
Santhosh K.
Last position:
Freelance Software Engineer at Zalando SE
- Drive migration of enterprise authorization platform from Styra DAS to open-source OPA via Skipper (Zalando's Golang-based ingress proxy) integration
- Optimise k8s resources and integrate native Prometheus metrics with OPA
- Migrate from internal monitoring solution to Prometheus CRs + Dash0
Tech Stack: Java/Kotlin, Golang, Python, Spring Boot, AWS, Kubernetes, Docker, OpenTofu, Prometheus, Grafana
Maciej R.
Last position:
Full Stack Developer (Freelancer) at Runbuggy
- Led development of RunBot AI assistant autonomously using LLM-powered workflow automation (React, TypeScript, Java, MongoDB, NATS)
- Architected TMS platform providing unified transportation management and real-time logistics visibility with AI processing pipelines
- Designed event-driven microservices architecture supporting marketplace
- Drove architectural decisions and technical leadership across full-stack platform development
Discover over 15,000 top freelancers
Statistics of experts using MongoDB
Aggregated from the professional profiles of matched freelancers.
Experience
16 years (Germany: 17 years)
Position duration
2.1 years
Positions per freelancer
9 (Germany: 11)
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Banking and Finance, Retail
Certification focus areas
Information Technology, Product Development, Business Intelligence
Bachelor's degree or higher
99% (Germany: 92%)
Master's degree or higher
66% (Germany: 57%)
Doctorate
6%
Certifications per freelancer
2
Most common languages
English, German, French
Speak two or more languages
95% (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 MongoDB
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.
MongoDB 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 (95%)
- Banking and Finance (44%)
- Retail (39%)
- Automotive (37%)
- Education (34%)
- Professional Services (32%)
- Media and Entertainment (30%)
- Manufacturing (27%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Document database
MongoDB is a document-oriented database built for applications that need flexible data structures, rapid iteration and horizontal scale. It stores records as BSON documents, allowing related data to stay together while schemas evolve with the product. Companies use it for content platforms, customer portals, commerce systems, mobile backends and event-driven services.
Data modelling
Strong MongoDB professionals turn business requirements into practical document models. They decide when to embed data, when to reference it and how to shape collections for real query patterns. Their work covers indexes, validation rules, aggregation pipelines, transactions and retention policies, with attention to consistency, access patterns and future changes.
Ecosystem and tooling
MongoDB fits into a broad application and cloud ecosystem. Specialists commonly work with MongoDB Atlas, replica sets, sharding, change streams and backup strategies, alongside drivers for JavaScript, Python, Java, Go or .NET. They also connect it with REST and GraphQL APIs, Kubernetes deployments, Kafka pipelines, observability tools and CI/CD workflows.
Common project work
Companies bring in freelance MongoDB expertise for focused delivery or to improve an existing data layer. Typical assignments include:
- Designing collections and indexes for a new product
- Migrating from a relational database or legacy document store
- Tuning aggregation pipelines and slow queries
- Setting up Atlas, replication, backups and disaster recovery
- Connecting MongoDB to APIs, event streams and analytics workflows
When to engage a specialist
Freelance support is useful when a team faces changing requirements, unreliable query performance or a migration with little room for downtime. It is also valuable when a product is moving from a single instance to replicated or distributed deployment. In Berlin, teams may combine remote delivery with on-site workshops, while agreeing early on communication language, release routines and access controls.
Signs of quality
A capable professional explains trade-offs instead of treating MongoDB as a universal replacement for relational databases. They inspect query plans, test realistic workloads and document operational decisions. Look for experience with failure recovery, security, monitoring and cost control, plus the ability to work closely with application, data and cloud specialists. Clear handover documentation is part of a strong result.
Frequently asked questions
What clients ask us most about MongoDB — answered in short.
MongoDB is used for applications that handle flexible, nested or rapidly changing data. Common examples include content management, product catalogues, customer profiles, real-time services, event processing and mobile backends. It is especially useful when application data maps naturally to documents.
MongoDB offers document-based storage and flexible schemas, while PostgreSQL and similar systems centre on tables, joins and relational constraints. The right choice depends on access patterns, transaction requirements, reporting needs and the maturity of the data model. A strong specialist can assess both options rather than assuming one is always better.
A capable MongoDB specialist should understand application development, API design, cloud infrastructure and data security. Useful adjacent skills include Atlas operations, Kubernetes, Kafka, observability, backup design and one or more official language drivers. These skills help connect the database to a reliable production system.
The required depth depends on the work. A small schema review may need focused database expertise, while a migration, sharded deployment or recovery redesign calls for a professional who has handled production operations. Ask candidates to describe comparable decisions, failure scenarios and measurable deliverables rather than relying on a generic technology list.
Yes. MongoDB work is often suitable for remote collaboration when repository access, environments, documentation and incident procedures are well defined. Berlin teams can use remote delivery for modelling and implementation, with on-site sessions reserved for architecture workshops, stakeholder alignment or sensitive migration planning.
Choose MongoDB when documents match the domain, fields evolve frequently or the application needs to retrieve related information together. A relational database may be preferable for complex joins, strict relational integrity or heavily structured financial reporting. The decision should follow workload testing and domain analysis.
Review the data model, index strategy, aggregation pipelines and operational documentation produced by the professional. Good work includes realistic query testing, clear backup and recovery procedures, secure access controls and monitoring for slow operations. Ask how the specialist would diagnose degraded performance before accepting a proposed fix.
Before working with MongoDB, clarify the current deployment, data volume, query patterns, uptime needs, compliance constraints and ownership of production access. Confirm whether the scope covers Atlas configuration, application changes, migration, testing or handover. Clear boundaries prevent database work from being separated from the systems that depend on it.
The average hourly rate of freelancers in Berlin, Germany who have used MongoDB in their recent projects is 85 €, which corresponds to a daily rate of about 677 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used MongoDB in their recent projects, 99% hold at least a Bachelor's degree, 66% hold at least a Master's degree, and 6% hold a doctorate.
On average, freelancers in Berlin, Germany who have used MongoDB in their recent projects have 16 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 MongoDB in their recent projects are English (99%), German (94%), and French (11%).
The most common industries among freelancers in Berlin, Germany who have used MongoDB in their recent projects are Information Technology (95%), Banking and Finance (44%), and Retail (39%).
The most common business areas among freelancers in Berlin, Germany who have used MongoDB in their recent projects are Information Technology (99%), Product Development (96%), and Business Intelligence (48%).
Main locations of FRATCH Experts, who have recently used MongoDB
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 
Stuttgart 
Dusseldorf 
Leipzig 
Dortmund 
Essen 
Nuremberg Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
Would you rather directly get in touch?
We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
