Azure Data Lake Storage Experts in Berlin
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Meet FRATCH Experts in Berlin, who have recently used Azure Data Lake Storage
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.
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
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.
Discover over 15,000 top freelancers
Statistics of experts using Azure Data Lake Storage
Aggregated from the professional profiles of matched freelancers.
Experience
12 years
Position duration
2.2 years
Positions per freelancer
7
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Automotive, Banking and Finance
Certification focus areas
Information Technology, Business Intelligence
Bachelor's degree or higher
100%
Master's degree or higher
83%
Certifications per freelancer
4
Most common languages
German, English, French
Speak two or more languages
100%
Based on our profile pool as of 16 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 Azure Data Lake Storage
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 16 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Azure Data Lake Storage 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 (83%)
- Automotive (50%)
- Banking and Finance (50%)
- Energy (33%)
- Media and Entertainment (33%)
- Pharmaceutical (33%)
- Professional Services (33%)
- Cosmetics (17%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Data lake foundation
Azure Data Lake Storage, commonly called ADLS or ADLS Gen2, is Microsoft Azure’s scalable storage layer for analytics data. It stores structured, semi-structured and unstructured files in hierarchical namespaces, making raw and curated data available to analytics, machine learning and reporting workloads. Companies use it as a governed foundation for lakehouse and enterprise data platforms.
Core capabilities
ADLS Gen2 combines Azure Blob Storage durability with file-system style organization and access control. Strong specialists configure containers, directory structures, lifecycle policies, redundancy and encryption to balance access, governance and operational needs. They also design ingestion and consumption patterns that keep storage efficient as data volumes and source systems change.
Connected ecosystem
The service works closely with Azure data and identity tools. Relevant expertise often includes:
- Azure Data Factory and Synapse pipelines for ingestion and orchestration
- Azure Databricks and Spark for transformation and large-scale processing
- Microsoft Purview for cataloging, lineage and governance
- Microsoft Entra ID, RBAC and POSIX-style ACLs for controlled access
- Power BI and SQL engines for analytics delivery
Typical workloads
Companies use ADLS for centralized reporting stores, streaming data platforms, machine learning datasets and archival repositories. It supports landing zones for ERP, CRM, IoT and application data, as well as curated layers for business intelligence. In regulated industries, teams also use it to separate raw evidence from transformed, access-ready information.
When specialists help
Freelance expertise is valuable when a company is moving from file shares or legacy warehouses, consolidating Azure data sources or introducing a lakehouse architecture. It is also useful when permissions have become unclear, pipelines are unreliable or storage costs are difficult to explain. Common deliverables include architecture decisions, migration plans, data structures, security models, deployment templates and operational runbooks.
Quality signals
Strong professionals understand both storage mechanics and the workloads that depend on them. They can explain partitioning, file formats, small-file handling, schema evolution, retention and performance trade-offs in business terms. They test access paths, automate infrastructure where appropriate and document how teams in Berlin or distributed across locations should operate the environment. Practical knowledge of Azure networking, monitoring, CI/CD and data protection is a clear advantage.
Frequently asked questions
Key details about Azure Data Lake Storage, drawn from the questions we get asked most.
Azure Data Lake Storage is used to store and organize large collections of structured, semi-structured and unstructured data for analytics. Companies commonly connect it to Azure Data Factory, Synapse, Databricks, machine learning services and Power BI.
ADLS Gen2 adds a hierarchical namespace and file-system style access controls to Azure Blob Storage. Blob Storage remains suitable for general object storage, while ADLS Gen2 is usually preferred when analytics workloads need directory operations, fine-grained permissions and scalable data processing.
A capable Azure Data Lake Storage specialist often works with Azure Data Factory, Synapse Analytics, Databricks, Spark, Microsoft Purview and Power BI. Identity and security skills around Microsoft Entra ID, RBAC, ACLs, private endpoints and network integration are also important.
Azure Data Lake Storage work can range from configuring a focused landing zone to designing a broad enterprise data platform. The right level of experience depends on source complexity, governance requirements, migration risk and the number of teams that will consume the data.
Azure Data Lake Storage projects are often suitable for remote collaboration because architecture, configuration, documentation and testing can be managed through shared repositories and Azure environments. On-site work in Berlin may still help when the project involves workshops with local stakeholders, sensitive systems or coordinated migration activities.
Ask a Azure Data Lake Storage specialist to explain a past storage design, including directory structure, permissions, partitioning, file formats, monitoring and recovery. Strong answers connect technical choices to workload performance, governance, cost control and the needs of data consumers.
Azure Data Lake Storage security depends on layered controls rather than a single setting. Review how the specialist handles Microsoft Entra identities, role assignments, ACL inheritance, encryption, private networking, secret management, audit logs and separation between development and production data.
ADLS commonly stores columnar formats such as Parquet alongside JSON, CSV and other source files. The best choice depends on query patterns and downstream tools, while effective specialists also address partition design, schema changes, compaction and the small-file problem.
The average hourly rate of freelancers in Berlin, Germany who have used Azure Data Lake Storage in their recent projects is 108 €, which corresponds to a daily rate of about 861 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used Azure Data Lake Storage in their recent projects, 100% hold at least a Bachelor's degree and 83% hold at least a Master's degree.
On average, freelancers in Berlin, Germany who have used Azure Data Lake Storage in their recent projects have 12 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 Azure Data Lake Storage in their recent projects are German (100%), English (100%), and French (17%).
The most common industries among freelancers in Berlin, Germany who have used Azure Data Lake Storage in their recent projects are Information Technology (83%), Automotive (50%), and Banking and Finance (50%).
The most common business areas among freelancers in Berlin, Germany who have used Azure Data Lake Storage in their recent projects are Information Technology (100%), Product Development (83%), and Business Intelligence (67%).
Main locations of FRATCH Experts, who have recently used Azure Data Lake Storage
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.
Countries:
Berlin 
Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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