Azure Data Lake Storage Experts in Germany
for secure analytics at scale, matched in minutes from over 15,000 CVs with the power of AIHire experts who design cloud data lakes, build reliable ingestion pipelines and connect Azure analytics services such as Synapse Analytics, Databricks and Power BI. FRATCH matches you quickly and precisely with vetted, available freelance professionals.
Meet FRATCH Experts in Germany, 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.
Alexander B.
Last position:
Senior Data Engineer at RWE AG
Architected and maintained data products for renewable energy operations, covering wind turbine, grid-meter, and weather data. Built scalable ETL/ELT pipelines in Azure Databricks using Delta Lake (bronze/silver/gold layers) and processed data in various formats, including structured and semi-structured data. Contributed to a data quality framework supporting table and column documentation, outlier detection, and completeness metrics across all datasets within a data product. In addition, implemented a DORA KPI Databricks dashboard used across all data products. Optimized CI/CD processes in Azure DevOps to streamline deployment across development, test, and production environments.
Technology stack: Azure Databricks, PySpark, SQL, Delta Lake, Unity Catalog, Azure Data Lake, APIs, Dremio, Azure DevOps, YAML, Git, Databricks Workflows, Application Insights, Terraform, OpenAI API, Codex, LLM-assisted workflows
Maximilian B.
Last position:
CTO at nikan.ai
Leading the technical vision and product strategy for a sovereign AI startup focused on European data infrastructure and compliance. Managing a cross-functional team of 7 across engineering, AI development, and operations in a fully remote environment.
- Defining the company's product and technology roadmap, including AI-powered solutions with integrated payment services
- Designing scalable platform architectures with emphasis on data sovereignty, security, and European regulatory compliance
- Driving hands-on development across the full stack while establishing engineering best practices and DevOps workflows
- Enabling developer productivity through mentoring, architectural guidance, and tooling decisions
- Shaping the long-term technical strategy to position the company for sustainable growth
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.
Lino G.
Last position:
Senior Data Scientist at VinFast Germany GmbH
- Led strategic software development of fusion algorithms for precise object tracking, trajectory prediction, and environment modeling based on multimodal sensor data (e.g., camera, LiDAR, radar, GNSS, IMU)
- Developed and implemented navigation algorithms for autonomous vehicles, including path planning, obstacle avoidance, and sensor fusion of visual, inertial, and distance-based sensor sources
- Automated extraction and training processes with CI/CD
- Developed and optimized data pipelines and processes in Microsoft Azure using Apache Spark, Databricks, and PySpark
- Developed and optimized embedded software for automotive control units
- Designed latency-critical software for real-time control in robotic systems with RTOS (freeRTOS, SAFERTOS)
- Used the Vector toolchain (CANdela, DaVinci, CANoe) for configuration and diagnostics
- Optimized existing data pipelines and processes (ETL, data warehouse, SQL)
- Developed and trained machine learning models using PyTorch
- Created deep-learning-based object detection and visual SLAM algorithms, trained on combined data from camera, LiDAR, and IMU sensors
- Implemented computer vision algorithms for object detection and classification in robotic systems using OpenCV and YOLO, utilizing synchronized image and depth data
- Implemented behavior-based control systems for autonomous robots using ROS2 Behavior Trees
- Performed testing, release, and integration of sensor fusion algorithms into automotive production programs
- Ensured adherence to proper software development processes and safety standards to guarantee high data quality (MISRA, ISO 26262, ASPICE)
Tobias L.
Last position:
Data Engineer at unitb consulting GmbH
Tasks: 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 different data sources
- Built monitoring and alerting systems for real-time platform monitoring
- Implemented data versioning and quality checks at every layer
- Designed automated test and deployment pipelines in GitLab and Bitbucket
Achievements:
- 2× 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, configuration errors to 0
- Delivered a complete end-to-end data platform at ~€10/month infrastructure cost
- Migrated 7 database tables with 0 downstream issues
- Removed 100% exposed credentials, eliminated external vendor dependency
- Delivered integration of 3 teams in 1 sprint
Rohit T.
Last position:
Senior Software Engineer at KRiAN GmbH
Clients: CARIAD, AUDI AG
- Built and deployed enterprise MLOps pipelines using Azure Machine Learning and Databricks. Reduced model release cycles by 95 percent, from four weeks to two days, through automated CI CD workflows.
- Delivered cloud native DevOps platforms for ADAS programs using Azure data services, Kubernetes, and Terraform based infrastructure provisioning.
- Designed high availability architectures with automated failover. Cut system downtime by 85 percent for mission critical energy trading platforms.
- Developed and integrated AI agents and enterprise chatbots using LangChain, AutoGPT, and GPT models. Enabled autonomous workflows and decision driven automation.
- Reduced cloud infrastructure spend by 40 percent through autoscaling strategies, spot instance usage, and policy driven resource governance across Azure and AWS.
- Partnered with Data Scientists, ML Engineers, Product Managers, and executive stakeholders to deliver large scale automotive and energy solutions.
- Implemented GitOps driven CI CD pipelines supporting automotive software delivery for over 500 engineers across distributed product teams.
- Designed and operated Kubernetes platforms on Azure AKS. Improved deployment stability and reduced rollback events by 70 percent.
- Implemented observability and monitoring stacks using Prometheus, Grafana, and Azure Monitor. Achieved 99.9 percent service availability targets.
Julia S.
Last position:
Senior Data Scientist / Consultant at Cloud Nation GmbH
Python, SQL, PySpark, Databricks, Databricks SQL, Delta Lake, dbt, Azure Data Lake Storage, Azure Machine Learning, Azure DevOps, Power BI, Git, MLflow
- Developed, validated, and optimized predictive analytics and classification models using Python (pandas), SQL, and modern ML frameworks.
- Performed data analysis, feature engineering, model validation, cross-validation, and stability analysis to ensure robust model quality and performance.
- Communicated model assumptions, results, uncertainties, and limitations to business units, management, and technical stakeholders.
- Built scalable data and machine learning workflows in cloud-based analytics environments using Databricks and Microsoft Azure.
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.
Ulm P.
Last position:
DataStage ETL Expert at ING Bank
- Datastage 11.7, dbt, Oracle 19, Python 3.12 / PySpark 3.5, Azure GitHub, Azure DevOps, Automic
- Development of migration jobs to transfer data from the collection DWH to the new Risk Mart, as well as development of ETL pipelines to migrate historical data from the old Mart to the new Risk Mart.
- Storage of the silver layer on Hadoop and the gold layer in Oracle.
- Translation of DataStage jobs into dbt to publish reporting data in Google Cloud to a PostgreSQL database.
- Creation and optimization of complex SQL queries for data extraction from a data vault, taking into account historical data in the point-in-time tables.
- Creation of Oracle table definitions (DDL) and adjustment of existing stored procedures.
- Versioning changes in GitHub and deployment via the CI/CD portal.
- Refactoring long-running DataStage jobs into Python using PySpark to reduce server load.
- Migration of SAS scripts to PL/SQL, including new development of distribution functions that have no direct equivalent in Oracle.
- Development of Automic jobs to run DataStage pipelines and Python scripts (PySpark jobs) that control the population of the SME and institutional risk tables in the Risk Mart and perform business calculations.
- Participation in the agile process, including creating user stories, estimations, and planning in Azure DevOps.
- Handling Azure DevOps tickets and close collaboration with testers and business teams for error analysis and resolution.
Ashkan Z.
Last position:
Microsoft Azure Senior Data Engineer / Senior Data Scientist at Vattenfall Europe
- Advising on the use of analytics and BI tools and services in the Microsoft Azure stack (e.g. MS Fabric, Synapse Workspaces and dedicated SQL pools, SQL Database, PostgreSQL, Snowflake, Databricks, Data Factory, SSIS, Analysis Services, Function Apps, Power BI, ML)
- Independently designing analytics solutions with Python, SQL, etc.
- Designing and implementing ETLs and data pipelines
- Creating and maintaining APIs
- Independently applying CI/CD, testing, and version control
- Data modeling
- Model development and optimization
- Anomaly detection with AI
- Predictive analytics
Used technologies:
- Snowflake
- Fabric
- Azure Synapse Analytics
- Azure DataFactory
- Azure Data Lake
- Azure DevOps
- Databricks
- Spark
- CI/CD
- SQL Database
- Python
- Power Platform
Stephan S.
Last position:
Senior Data/ML Consultant & Technical Lead at Jolin.io
Role: Software Engineer & Applied Mathematician (Mathematical optimization for scheduling; duration: 1 months; team setting: Team of 2, remote; technologies: JuMP, Julia, Pluto, Svelte, JavaScript, TypeScript, JetBrains Space, Terraform, Nomad)
Role: Software & Cloud & Web Engineer (Building scalable data science compute cluster from scratch; duration: 11 months; team setting: Team of 1, on-site; technologies: Terraform, Kubernetes, k8s ingress, k8s services, k8s RBAC, k8s networking, k3s, etcd, S3, DNS, certificates, Julia, Pluto, JavaScript, Tailwind, Astro, npm, Parcel, Preact, MUI, JWT, AWS SQS, AWS RDS, Python, GitLab, GitHub)
Role: AI & Web Engineer (Custom ChatGPT service; duration: 1 months; team setting: Team of 2, remote; technologies: Python, Poetry, LangChain, Tailwind, ChatGPT API, Flask, FastAPI)
Role: Architect & Data Engineer (Central datalake setup and ingestion; duration: 9 months; team setting: Team of 5, remote; technologies: Infrastructure-as-code, AWS CDK, Python, Boto3, PySpark, AWS Glue, IAM, S3, ECS, Fargate, Lambda, Apache Hudi, DeltaLake, Databricks, GitHub, Jira, Miro)
Role: Software Engineer (PoC Julia migration of scikit-decide; duration: 1 months; team setting: Team of 2, remote; technologies: Python, Julia, GitHub)
Jorge M.
Last position:
Data Architect at Deutsche Bahn
- Design and provide best practices on data modeling for dbt, including changing dimensions, late arriving data handling, and testing
- Design the ingestion flow from other systems into S3 and Redshift
- Design and implement new partitions for Dagster and incremental loading with dbt
- Map business requirements to technical architectures
- Instruct junior team members
Marco P.
Last position:
Senior Siebel CRM and BI Architect
- Maintenance and enhancement of a Siebel CRM Service & Marketing implementation (Siebel 23.1, OpenText, OBIEE, Informatica).
Maziyar K.
Last position:
Data Engineer at MSD Germany
- Lead Architect to design and implement the data lake and ETL Pipeline using AWS Stack
- Performance Optimization of Data Ingestion of ETL Pipeline
- Development of Data Validation using Great Expectations
- Leading of the data migration for two sources exchanges
- Data Modeling in AWS Redshift
MLOps
- Model inference implementation by mlflow and AWS SageMaker
- Feature Engineering for the running ML Models ( Recommender Engineer, Clustering )
- Implementatino of Model Registry and artifactory using mlflow
- Historization an Profiling of the Input Data Using AWS Glue Crawler and AWS Data Catalog
- Feature importance using mlflow
Tech. Stack: Python 3, AWS Glue, AWS Step Fucntion, AWS Lambda, AWS EventBridge, AWS IAM Role, AWS SageMaker, AWS EC2, AWS Glue Crawler, AWS CloudWatch, MLFlow, ETL, Data lake, GitHub Action, Terraform, Jenkins, Ansible playbooks (Infrastructure as Code), CI/CD, GitLab, SQL, PySparkSCRUM, Agile, Jira, BigData, VSCode, DBeaver, MSSQL, MySQL, grafana, Docker, Linux, Bash, MapReduce, Data Modeling (ORM), Pandas, YAML, SQL-Alchemy
Discover over 15,000 top freelancers
Statistics of experts using Azure Data Lake Storage
Aggregated from the professional profiles of matched freelancers.
Experience
15 years
Position duration
2.1 years
Positions per freelancer
10
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Banking and Finance, Energy
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
100%
Master's degree or higher
69%
Doctorate
19%
Certifications per freelancer
4
Most common languages
German, English, French
Speak two or more languages
96%
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 Germany 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 Germany 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 19 Sep 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 (89%)
- Banking and Finance (48%)
- Energy (44%)
- Professional Services (44%)
- Automotive (33%)
- Transportation (30%)
- Retail (30%)
- Education (26%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Azure Data Lake Storage does
Azure Data Lake Storage, commonly called ADLS or ADLS Gen2, is Microsoft Azure storage for large volumes of structured, semi-structured and unstructured data. It combines Blob Storage with a hierarchical namespace, making it suitable for data lakes, analytics platforms, machine learning data and long-term archives.
Core data lake design
Strong specialists define storage zones, folder structures, naming rules and lifecycle policies that keep a lake usable as it grows. They configure access with Microsoft Entra ID, role-based permissions and shared access controls, while separating raw, prepared and curated data for dependable downstream use.
Azure ecosystem and tooling
ADLS Gen2 commonly works alongside Azure Data Factory, Synapse Analytics, Azure Databricks, Microsoft Fabric and Power BI. Professionals may also use Spark, Delta Lake, Event Hubs, Azure Functions, Purview and infrastructure-as-code tools to ingest, transform, catalog and govern data across the environment.
Typical delivery work
- Design a medallion or zone-based lake architecture
- Migrate file shares, databases and Blob Storage into ADLS Gen2
- Build batch and streaming ingestion pipelines
- Tune Spark and Synapse access to lake data
- Establish cataloging, lineage, retention and recovery processes
When companies need specialists
Freelance expertise helps when a data lake must be created, restructured or connected to existing systems without disrupting reporting. It is also useful when permissions are unclear, ingestion is unreliable, storage costs are difficult to control or analytics teams cannot trust the available data. In Germany, remote delivery often works well, with on-site workshops added when security or stakeholder coordination requires them.
What distinguishes strong professionals
The best professionals understand storage architecture as well as data engineering, security and operations. They can explain why ADLS Gen2 is preferable to a basic object store for a given workload, document decisions, automate repeatable deployments and test access boundaries. Experience with regulated industries, German-speaking stakeholders or distributed Azure teams can further support smooth collaboration.
Frequently asked questions
Need clarity? These are the questions we hear most often about Azure Data Lake Storage.
Azure Data Lake Storage is used to collect and retain data from applications, databases, devices and business systems in a central cloud repository. Companies use it as a foundation for reporting, advanced analytics, machine learning and data transformation.
ADLS Gen2 adds a hierarchical namespace and granular directory-level access to Azure Blob Storage capabilities. That structure supports analytics workloads and large data sets more effectively, while Blob Storage may be sufficient for simpler object storage needs.
A capable Azure Data Lake Storage specialist often brings experience with Azure Data Factory, Synapse Analytics, Databricks, Spark, Delta Lake and Microsoft Entra ID. Knowledge of data governance, networking, security and infrastructure automation is also valuable.
The right ADLS Gen2 professional depends on the project’s scope, not a fixed career duration. A simple migration may need focused storage and pipeline expertise, while a governed enterprise lake requires architectural judgment, security knowledge and experience integrating several Azure services.
Yes. Azure Data Lake Storage is managed through cloud tools, so architecture, configuration, pipeline work and documentation can usually be handled remotely. On-site sessions may still help with workshops, security reviews or coordination across German-speaking teams.
Ask an ADLS Gen2 specialist to explain a comparable lake design, including partitioning, permissions, lifecycle management, monitoring and recovery. Strong answers connect technical choices to query performance, governance, operational effort and the needs of data users.
Azure Data Lake Storage projects should address identity management, role-based access, private networking, encryption, audit logs and separation between data zones. A specialist should also define how sensitive data is cataloged, retained and accessed by pipelines and teams.
ADLS can store data arriving from both batch and streaming processes, but it is not itself the complete streaming solution. Professionals commonly combine it with Event Hubs, Stream Analytics, Databricks or other processing services to land and transform events reliably.
The average hourly rate of freelancers in Germany who have used Azure Data Lake Storage in their recent projects is 100 €, which corresponds to a daily rate of about 802 € based on an 8-hour working day.
Of the freelancers in Germany who have used Azure Data Lake Storage in their recent projects, 100% hold at least a Bachelor's degree, 69% hold at least a Master's degree, and 19% hold a doctorate.
On average, freelancers in Germany who have used Azure Data Lake Storage in their recent projects have 15 years of professional experience, with a single engagement typically lasting around 2.1 years.
The most common languages among freelancers in Germany who have used Azure Data Lake Storage in their recent projects are German (100%), English (96%), and French (15%).
The most common industries among freelancers in Germany who have used Azure Data Lake Storage in their recent projects are Information Technology (89%), Banking and Finance (48%), and Energy (44%).
The most common business areas among freelancers in Germany who have used Azure Data Lake Storage in their recent projects are Information Technology (100%), Business Intelligence (81%), and Product Development (74%).
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.
Berlin Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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