
Data Lake Experts in Berlin
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Meet FRATCH Experts in Berlin, who have recently used Data Lake
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.
Nisanthan S.
Last position:
Business Intelligence Consultant (freelance) at NBIC – Nisanthan BI Consulting
Advising companies on building, migrating and optimising BI and reporting landscapes (Power BI, SQL, Python, ETL)
5 client engagements in real estate and finance since 05/2025: taking over and stabilising existing reporting, automating recurring standard and management reports, building cash-flow models
Proposal and feasibility assessments for BI and reporting projects
Using AI-assisted development (Claude Code) to accelerate automation, tooling and web/app development
Custom ERP system
Problem: A client's core processes ran on scattered, siloed Excel files with no central data storage – error-prone, hard to scale and impossible to analyse end-to-end.
Approach: Captured the business processes and requirements, modelled the data and developed iteratively together with the business team.
Implementation: Built a tailored, web-based ERP system with a central database, role-based modules and automated reporting – delivered using AI-assisted development in Claude Code.
Timesheet app
Starting point: Time tracking based on an overgrown, macro-heavy Excel template – maintenance-intensive, single-user and error-prone.
Implementation: Migrated all functionality and VBA macros into a standalone web app with central data storage, multi-user support and automated reporting.
Cash-flow modelling
Starting point: The existing cash-flow model covered standing investments only; project developments were missing from steering.
Implementation: Built and extended the CF model to include project-development cash flows.
Optimisation: Reviewed and optimised existing CF models and expanded the KPI outputs for reporting and steering.
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.
Jan K.
Last position:
Data Expert at Manufacturing
Vili D.
Last position:
Technical Lead, Data Engineer at Mercedes-Benz Consulting
- Optimized the data architecture (medallion) to better decouple processing stages and improve transparency and reproducibility
- Ensured technical quality of data processing in Databricks by introducing schema enforcement, data quality checks and a structured data architecture
- Orchestrated pipelines with Azure Data Factory
- Professionalized and automated the development and deployment process by integrating Git and GitHub Actions
- Led the Data Engineering team (3 members) in a functional role
- Conducted workshops to optimize and stabilize the data platform and the development process
- Collected and prioritized new requests, 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
Bertrand R.
Last position:
Interim IAM Product Owner (Identity Management) at REWE digital GmbH
- Establishing Identity & Directory Management as a new (split-off) team & product within the IAM cluster.
- Leading the “Identity & Directory Management” product (8 people) as Product Owner.
- Concept for ‘Digital Identities’, i.e. IDs with n users/accounts and strategy for a modernized product offering.
- Upgrading APIs, migrating to a containerized infrastructure, rolling out international markets & standardized solutions across the REWE enterprise group.
- Tech: OpenText™ (NetIQ) eDirectory & Identity Manager, LDAP, SAP HR/HCM, Docker/Kubernetes/Podman, REST APIs, Microsoft Active Directory & Entra ID, postgresDB, Keycloak, Ansible, Cyberark (PAM), Apache Kafka, Jira, Confluence, Miro.
Viktoria B.
Last position:
Freelance Global Recruiter IT & Engineering at Freelance Global Recruiter IT & Engineering
- Active Sourcing (Europe & Asia)
- End-to-end recruiting (Europe & Asia): coordination of recruitment processes; consulting on recruitment strategy
- Collaboration with hiring managers incl. C-level and Global Vice President
- Preparation of contracts and discussion of all questions with candidates
- Training and mentoring on interview techniques, active sourcing, self-organization, and pipeline building
- Many years of broad work experience with staffing and recruitment agencies
- Collaboration with global HR departments incl. works council
- Very good user knowledge of MS Office 2011 (Word, PowerPoint, Excel)
- Very good user knowledge of CRM/ATS systems (Salesforce, Personio, iCIMS, HCL Notes)
Amogha S.
Last position:
Senior Product Manager - OS, platform, IAM at Aleph Alpha GmbH
- Leading the product lifecycle for sovereign AI platform and operating system teams for enterprise & government clients and internal stakeholders (infra, solution delivery, support, revenue)
- Built and scaled the platform from a 200-user beta to a full rollout of 70K+ members at the Bundesagentur für Arbeit (BA), secured with ISO 42001 and EU AI Act compliance
- Architected the shift to a multi-tenant shared inference, increasing GPU cluster utilization from 20% to 85% and reducing infrastructure cost-to-serve by 40% for SaaS clients
- Shipped model quantization, allowing clients to run advanced LLMs on legacy hardware (A100s GPUs) instead of the H100s, saving upwards of 70% cost per enquiry
- Abstracted complex Helm configurations into a dynamic model manager, reducing the time to install or swap models by ~80%
- Killed an expensive move to build own dashboard service, pivoting to an API-first data strategy that clients can consume directly and saving €100Ks in opex and capex
- Built a safety-first agent marketplace and control plane lighthouse project for a Tier-1 bank, allowing internal teams to deploy autonomous agents within strict regulatory guardrails
Mohamed G.
Last position:
Lead / Principal Cloud, AI & Security Architect at Freelancer / CC Conceptualise GmbH
Projects:
Project: RWE – Development of a company-wide Zero Trust cybersecurity architecture (CITADEL) Role: Senior Enterprise Cybersecurity Architect / Zero Trust Architect Company: RWE AG Description: Concept and implementation of the strategic CITADEL cybersecurity target architecture at RWE, based on the Zero Trust architecture principle and aligned with regulatory requirements such as NIS2, ISO 27001 and company-wide security governance policies. The goal was to build a measurable, auditable and scalable security architecture with a strong focus on Identity Governance, compliance transparency and operational manageability. Responsibilities & Achievements:
- Zero Trust architecture design: Developed a company-wide Zero Trust reference architecture (Identity, Device, Network, Application, Data) including trust zones, control points and enforcement mechanisms according to NIS2.
- Identity & Access Governance (IGA): Designed and introduced IGA governance structures including role models, recertification processes, segregation of duties (SoD) and lifecycle management for identities and access.
- Security governance & KPIs: Defined and implemented security KPIs and metrics to manage Zero Trust maturity, identity risks and compliance at the management level.
- Compliance & reporting: Built standardized compliance reports and dashboards to support internal audits, external assessments and regulatory evidence (e.g. NIS2).
- Architecture & stakeholder alignment: Worked closely with Enterprise Architecture, IT operations and business units to integrate the CITADEL architecture into existing IT and security landscapes.
- Strategic security consulting: Advised programs and projects on Zero Trust compliance, identity centricity and regulatory requirements in the energy and critical infrastructure (KRITIS) environment. Technologies & Methods: Zero Trust Architecture, NIS2, Identity Governance & Administration (IGA), IAM, RBAC, SoD, Entra ID, SailPoint, Zscaler, Terraform / IaC, Policy as Code, security KPIs, compliance reporting, NIST 2.0, ISO 27001, Enterprise Security Architecture, governance frameworks, risk & control management
Project: Scalable AI Workbench Platform on Microsoft Azure Role: Cloud Architect & Engineer Company: Siemens Energy Description: Design, development and operation of a secure, modular cloud infrastructure to support Data Science, Machine Learning and AI applications for various engineering teams at Siemens Energy. Responsibilities & Achievements:
- Cloud architecture: Designed and implemented an Infrastructure-as-Code solution (Terraform) for automated provisioning of Azure resources (Resource Groups, Storage Accounts, Cosmos DB, Application Insights, networking, PostgreSQL Flexible Server, Azure Container Apps, Azure Container Registry).
- Developer portal: Used Backstage with custom frontend and backend plugins (Node.js, TypeScript, React.js, PostgreSQL, Container Apps) to enable self-service and empower developers, data scientists and AI/ML engineers.
- Role-based access control: Implemented Azure RBAC to grant targeted access (e.g. Storage Blob Data Contributor, Reader) to engineering groups (e.g. AI Engineers) for relevant resources.
- Data platform engineering: Built and configured a multi-layered storage landscape (Raw, Curated, Vector data), including automated container creation and access control for advanced analytics and AI workloads.
- DevOps integration: Integrated with Azure DevOps for CI/CD pipelines to automate deployment, monitoring and compliance.
- Security & compliance: Implemented Private Endpoints, network policies and Managed Identities to ensure data protection and regulatory compliance.
- Collaboration: Worked closely with cross-functional teams to align the cloud infrastructure with business and technical requirements and drive digital transformation at Siemens Energy. Technologies: Azure, Terraform, Azure DevOps, Cosmos DB, Application Insights, Azure Storage, Private Endpoints, Azure Synapse, Azure Machine Learning, Azure Entra ID, RBAC, Backstage, Node.js, React.js, PostgreSQL, Python (automation), Git
Hai D.
Last position:
Principal System Architect & Tech Lead at IU International University of Applied Sciences
Leading architecture and delivery of AI-powered educational content platform, managing two development teams with full technical ownership.
Designed and architected the Content Hub platform replacing legacy SiteFusion systems, enabling professors to create AI-assisted learning materials for improved student outcomes.
Established technical strategy, defined architecture requirements, and aligned two cross-functional teams (AI Media Team, TEAQ Team) on a unified delivery roadmap.
Implemented Clean Architecture principles and AI-agent-friendly documentation standards across the engineering organization.
Drove adoption of modern development workflows including CI/CD automation and MongoDB-based content management solutions.
Muskan V.
Last position:
AI Engineer at Sagas IT Analytics
- Built an AI Research Assistant with RAG, LangChain, LangGraph, and OpenAI LLMs integrated with vector search; cut research time by 30%.
- Designed custom retrieval workflows with LlamaIndex, building a ReAct-style agent for dynamic chunking; improved query accuracy by 18%.
- Researched and optimized embedding strategies, reducing retrieval cost/query by 15%.
- Developed RAG evaluation frameworks using RAGAS and Langsmith with custom datasets; improved coverage by 40%.
- Fine-tuned LLMs (LLaMA 2 on Vertex AI with custom inference containers, dynamic batching, and quantization); reduced inference latency by 25%.
- Integrated AI agents in LangGraph with short-term & long-term memory (Mem0); increased task completion rate by 20%.
- Created schema-aware synthetic data generators; fine-tuned downstream models achieving +12% F1 score.
Germo G.
Last position:
BI Developer at Rhenus Logistics
- Business intelligence development in the logistics sector.
- Migrating existing reporting and analysis solutions from Cognos to Microsoft BI.
- Using Microsoft SQL Server, SSAS, Power BI, and other Microsoft BI platform components.
Christian R.
Last position:
Freelance Data Engineer at Ingenieurbüro Christian Richter – Data, Cloud & Container
- Contributed to over 20 successful projects
Ilya I.
Last position:
Data/Platform/Software Engineer/SRE at IT Consulting
- Designed a platform based on IoT, Azure, Kubernetes, and Postgres for an existing application
- Migrated from "click-ops" and UI-defined CI/CD pipelines to infrastructure-as-code with Terraform, enabling complete redeployment of multiple environments
- Technologies: Terraform, OpenTofu, Azure, Azure DevOps, Kafka, IoT, Kubernetes, Grafana, Prometheus, GitOps, relational databases
Discover over 15,000 top freelancers
Statistics of experts using Data Lake
Aggregated from the professional profiles of matched freelancers.
Experience
16 years (Germany: 18 years)

Position duration
2.2 years (Germany: 2.4 years)

Positions per freelancer
12 (Germany: 10)

Top business areas
Information Technology, Business Intelligence, Product Development

Top industries
Information Technology, Professional Services, Automotive

Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
92% (Germany: 95%)
Master's degree or higher
75% (Germany: 70%)
Doctorate
17% (Germany: 18%)

Certifications per freelancer
3

Most common languages
English, German, French

Speak two or more languages
100% (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 Data Lake
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.
Data Lake 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 (86%)
- Professional Services (50%)
- Automotive (43%)
- Banking and Finance (29%)
- Transportation (29%)
- Retail (29%)
- Education (21%)
- Energy (21%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What a Data Lake Does
A data lake stores raw structured, semi-structured, and unstructured data in its original form. Companies use it as a central foundation for analytics, reporting, machine learning, research, and operational insight. Unlike a traditional warehouse, it can accept varied data before a fixed schema is defined.
Core Architecture
A reliable data lake separates storage, processing, cataloguing, and access. Strong specialists choose suitable object storage, define zones for raw and curated data, and create clear ownership rules. They also design partitioning, file formats, metadata, retention, and access controls so the lake remains usable as it grows.
Ecosystem and Tooling
Common environments combine cloud object storage with processing and orchestration services. Relevant skills may include Apache Spark, Apache Kafka, dbt, Trino, Databricks, Snowflake, Delta Lake, Apache Iceberg, or Apache Hudi. Professionals also work with Python, SQL, infrastructure automation, data catalogues, observability, and identity management.
Typical Projects
- Consolidating data from applications, devices, APIs, and business systems
- Building batch and streaming ingestion pipelines
- Creating curated datasets for BI and machine learning
- Migrating on-premise repositories to cloud storage
- Introducing lakehouse patterns with reliable table management
These projects often connect a data lake to warehouses, dashboards, feature stores, and real-time services. The right design depends on data freshness, query patterns, compliance needs, and the teams consuming the information.
When to Bring in Specialists
Companies usually need freelance expertise when a lake has become difficult to govern, pipelines fail silently, or users cannot find trusted datasets. A specialist can assess the current architecture, define a practical target state, and deliver ingestion, migration, quality, or access-control improvements. In Berlin, remote collaboration is common, while some projects benefit from on-site workshops with local product and data teams.
What Good Work Looks Like
Strong professionals make data discoverable, traceable, secure, and cost-conscious. They document decisions, test transformations, monitor freshness and failures, and separate experimentation from trusted production data. They can explain trade-offs between a data lake, warehouse, and lakehouse in clear business terms and leave behind maintainable pipelines and operating practices.
Frequently asked questions
Need clarity? These are the questions we hear most often about Data Lake.
A Data Lake is used to store and process large volumes of raw data from many sources. Companies use it for analytics, machine learning, reporting, event processing, and future use cases that may not have been defined when the data was collected.
A Data Lake usually stores varied data in its original form, while a data warehouse typically applies a structured schema before analysis. A lake offers flexibility for exploration and machine learning; a warehouse often provides more predictable performance for governed business reporting.
A Data Lake focuses on flexible, low-cost storage, while a lakehouse adds warehouse-style table management, transactions, governance, and reliable analytics on that storage. Technologies such as Delta Lake, Apache Iceberg, and Apache Hudi are often used to bring these capabilities to lake architectures.
A strong Data Lake specialist usually brings skills in SQL, Python, cloud storage, data modelling, orchestration, and distributed processing. Experience with Apache Spark, Kafka, Databricks, catalogues, security, infrastructure automation, and data quality is also valuable.
The right level of Data Lake experience depends on the scope, risk, and existing architecture rather than a fixed number of years. A small ingestion project may need focused pipeline expertise, while a company-wide platform requires proven work with governance, migration, operations, and stakeholder alignment.
A Data Lake project can often be delivered remotely from Berlin when documentation, access, and communication routines are clear. On-site sessions may still help with architecture workshops, source-system discovery, and coordination across teams, especially when German-language collaboration is important.
Ask a Data Lake professional to explain a past architecture, including ingestion choices, data quality controls, security, monitoring, and failure recovery. Look for clear trade-offs and practical documentation, not only familiarity with a particular cloud service or processing tool.
A Data Lake can become a disorganized data dump when ownership, metadata, quality rules, and access policies are missing. Strong specialists prevent this with defined zones, cataloguing, lifecycle controls, observable pipelines, and clear standards for promoting data into trusted products.
The average hourly rate of freelancers in Berlin, Germany who have used Data Lake in their recent projects is 109 €, which corresponds to a daily rate of about 872 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used Data Lake in their recent projects, 92% hold at least a Bachelor's degree, 75% hold at least a Master's degree, and 17% hold a doctorate.
On average, freelancers in Berlin, Germany who have used Data Lake in their recent projects have 16 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 Data Lake in their recent projects are English (100%), German (93%), and French (14%).
The most common industries among freelancers in Berlin, Germany who have used Data Lake in their recent projects are Information Technology (86%), Professional Services (50%), and Automotive (43%).
The most common business areas among freelancers in Berlin, Germany who have used Data Lake in their recent projects are Information Technology (100%), Business Intelligence (71%), and Product Development (71%).
Main locations of FRATCH Experts, who have recently used Data Lake
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.
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