Data Lake Experts in Frankfurt
to turn complex data into insight, matched in minutes with vetted and available freelancersHire experts who design scalable data platforms, build reliable ingestion pipelines and connect analytics, machine learning and governance workflows. FRATCH matches you quickly and precisely with vetted, available freelancers for your Data Lake project.
Meet FRATCH Experts in Frankfurt, who have recently used Data Lake
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.
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
Helge B.
Last position:
Solution Architect at Deutsche Bahn
- Responsible for the end-to-end application and integration architecture for the S-Bahn Hamburg maintenance digitalisation program. A highlight is the introduction of robots and permanently installed camera gates to automate vehicle inspections, allowing AI image algorithms to assess the condition of trains during operation.
- Deutsche Bahn has one of the largest SAP PM implementations in the world, which is a central component of this digitalisation.
Justina K.
Last position:
Freelance Consultant for Change & Data Transformation at Freelance Fast Data Consulting
Project, Strategic Consulting – building the Data Strategy and Data Governance Policy for the German branch, client (private bank Julius Bär, headquarters Zurich), March 2026 – present
- Design and negotiation of the data strategy with key stakeholders, including obtaining board sign-off (strategic consulting) – in this context, regulatory advice on data regulations in the EU and specifically for Germany. The data strategy includes: Data Lifecycle Management: data capture, data storage, data usage, data retention policy, data quality incident management
- Definition of milestones and technical feasibility for implementing TOM for the data strategy, data quality checks, metrics, and a metadata inventory to ensure the bank’s compliance with DORA, BCBS239, and MaRisk requirements.
Core project data change, client: (ING Bank, Frankfurt am Main), March – December 2025
- Concept development and solution design for new end-to-end processes including technical interfaces
- Definition of synchronization logic and data flows between legacy and target systems (decommissioning of legacy systems)
- Analysis and validation of data models
- Stakeholder communication with product owners, feature engineers, UX designers, and operational teams for decision-making
- Analytics and impact assessments, e.g. to assess downstream effects and regulatory requirements
- Documentation and comments on technical and business requirements to support implementation in agile squads
Project digitalization of a user group, client: (ING Bank, Frankfurt am Main), as Interim Product Owner, Jan 2025 – present
- Co-shaping key decisions on data architecture and process logic in the context of historized data and user login functionality
- Development of business solution concepts for migration to the target system, including system integration and data flows
- Support with analytics and impact analyses, especially regarding the ability to provide information to law enforcement authorities
- Active coordination with stakeholders from different squads to support decision-making and ensure regulatory requirements are met
- Creation of test scenarios for operational teams and backend systems in the area of API management using Postman and Bruno.
Ulf S.
Last position:
Innovation Manager at Claas
- Steer innovation projects for AI-based systems, focusing on plant detection technology for agricultural applications.
- Champion digital transformation initiatives within software departments, advocating for Agile methodologies and Scrum-based workflows.
- Analyse and optimise team infrastructure, fostering a culture of continuous improvement and strategic development.
- Pioneered integration of AI technology in agricultural equipment, significantly advancing precision farming techniques.
- Designed a cloud-based dashboard platform, facilitating real-time data access and decision-making for field management.
- Developed camera-based GPS systems for tractors, enhancing field analysis capabilities.
- Created and implemented AI interfaces for pre-processing image data, ensuring seamless statistics delivery to the cloud via LTE.
- Innovated cloud-based dashboard platforms, introducing cutting-edge technology concepts and coordinating cross-functional teams to execute project visions.
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
Roman K.
Last position:
Senior Data Engineer / Cloud Architect at DB Systel
- Development of a central billing app for cloud costs at DB
- AWS
- Python
- AWS CDK
- RDS
- Spark (PySpark)
- Glue
- Lambda
- CI/CD (GitLab)
- React/Typescript
- data optimization
- Scrum
Petru K.
Last position:
Architect & Technical Team Lead & Senior Developer at Goetel GmbH
- Design, architecture & development/programming of ETL/ELT data pipelines, DWH, BI solution
- Technical project lead, POC – proof-of-concept creation
- Liaison between business units and technical teams
- Azure DevOps Boards & Jira
- Data modeling & data engineering – data warehouse & data mart
- Azure (Data Factory, Azure SQL, Azure DevOps CI/CD, Azure Data Lake V2, Business Central REST API, OData API, OAuth2 tokens)
- SharePoint lists & API for ADF, Firebird DB, Postgres DB, DB2
- Power BI (Power Query), DAX, Excel PBI add-on, GIS data
- Automated ETL process monitoring/logging, performance monitoring, error monitoring – capturing & resolution
- Index performance tuning & statistics monitoring, Transact-SQL
- Data security – MFA (multi-factor authentication) & OAuth2, MS Graph, Azure networks & firewalls, gateways, roles, user groups – with read/write permissions
- Sources – Vario Bill, Camunda, Radius, Geo Database, OTRS, PAST, MS Dynamics Business Central, Azure Blob Data Lake, SharePoint lists
Ritika S.
Last position:
AWmOpsRtKekEX(CPEliRenIEtN: CInEfoSrs.yDs,aHtaitAarcchhiiEtencetr(gAyW) S)
Global marketing analytics for Hitachi Energy as part of a global data modernization initiative aiming to enhance data retention, historical data availability and provide Eloqua's 2-year retention for remote interaction reporting and analytics.
Analyzed Eloqua's default retention policy and identified risk of data loss for records older than two years.
Designed and implemented historical data preservation strategy by creating transformed tables in the target data platform to archive older data while ensuring data quality dashboards.
Collaborated with the Power BI team to re-point dashboards from raw Eloqua imports to the newly created archival layer.
Leveraged Jira to track and manage data engineering tasks, bugs, and feature requests across Agile sprints; coordinated backlog prioritization and task assignment to align data pipeline development with business needs.
Power BI dashboard optimization:
Worked closely with business stakeholders to assess and understand reporting needs for reverse customer data.
Designed and implemented incremental refresh in Power BI to ensure daily updates without full data reloads.
Collaborated with Azure data engineers to optimize data processing and publication pipelines.
Stakeholder communication & data modeling:
Acted as liaison between Group Data Office and Technology Office to align data modelling standards.
Gathered requirements from data engineering team and participated in weekly status meetings to provide implementation updates and resolve blockers across teams in Germany, Poland, and India.
Documentation & quality assurance:
Prepared end-to-end technical design documentation, data flow diagrams, and Power BI audit guides for future reference.
Participated in UAT sessions with business users to validate data outputs and report accuracy.
Aparna V.
Last position:
Data Manager at University of Cologne
- Engineered and automated a data pipeline using GitLab CI/CD for data ingestion, validation, and loading into a central database.
- Developed Python scripts for data validation and transformation, ensuring data quality and compliance with metadata standards.
- Managed the entire data lifecycle from file-based repositories to a structured SQL Server database.
- Worked in an interdisciplinary team to establish a central database for-omics data and ensure reproducibility of computational analyses.
Daniel S.
Last position:
Business and IT consulting at Business and IT Consulting (freelance)
- Process consulting
- Project management
- Creating and aligning functional and technical specifications
- Portfolio management
- Stakeholder management
- Data cleansing
- Test, quality, and requirements management
- Rollout management
- Financial planning
- Marketing and sales consulting
- IT architecture and strategy consulting
- Trainer for IT, project management, and eBusiness
Discover over 15,000 top freelancers
Statistics of experts using Data Lake
Aggregated from the professional profiles of matched freelancers.
Experience
22 years (Germany: 18 years)
Position duration
2.8 years (Germany: 2.4 years)
Positions per freelancer
12 (Germany: 10)
Top business areas
Information Technology, Business Intelligence, Project Management
Top industries
Information Technology, Healthcare, Banking and Finance
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
100% (Germany: 97%)
Master's degree or higher
50% (Germany: 70%)
Doctorate
20% (Germany: 18%)
Certifications per freelancer
4
Most common languages
German, English, French
Speak two or more languages
100% (Germany: 96%)
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 Frankfurt 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 Frankfurt 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 29 Oct 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 (82%)
- Healthcare (73%)
- Banking and Finance (55%)
- Professional Services (55%)
- Energy (45%)
- Insurance (36%)
- Transportation (36%)
- Pharmaceutical (36%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What a Data Lake does
A Data Lake stores structured, semi-structured and unstructured data in its original or lightly processed form. It supports exploration, reporting, machine learning and operational analysis from a shared data foundation. Unlike a narrow warehouse, it can retain varied sources before their final use is known.
Core architecture
A strong design separates ingestion, storage, processing, cataloguing and consumption. Object storage such as Amazon S3, Azure Data Lake Storage or Google Cloud Storage often forms the foundation, while formats such as Parquet and Apache Iceberg improve performance, schema control and table management. Security and lifecycle rules belong in the design from the start.
Ecosystem and skills
Professionals may work across cloud services, distributed processing and orchestration tools, including Apache Spark, Kafka, Airflow, Databricks and Snowflake integrations. They understand batch and streaming pipelines, SQL, Python, APIs, metadata, lineage and access policies. Familiarity with data mesh or lakehouse patterns can help when ownership is distributed.
Where companies use it
- Consolidating ERP, CRM, application and IoT data
- Preparing datasets for machine learning and advanced analytics
- Supporting finance, logistics, manufacturing and life sciences reporting
- Creating governed self-service access for analysts
In Frankfurt, companies often combine cloud data services with existing enterprise systems and strict internal controls. Freelance specialists can support architecture, migration or delivery without requiring a permanent internal team.
When to bring in specialists
Bring in external expertise when data sources multiply, pipelines become fragile or teams cannot agree on ownership and definitions. A specialist can assess the current estate, select storage and processing patterns, establish quality checks, and deliver a working platform with clear documentation. They can also help move from a raw lake toward a governed lakehouse.
What good work looks like
Strong professionals make trade-offs visible. They define schemas and contracts, design replayable pipelines, monitor freshness and failures, and control access at data and table level. They test costs and performance with realistic workloads, explain decisions clearly, and leave maintainable infrastructure for the teams that will operate it.
Frequently asked questions
Before you brief your next project: the most common questions about Data Lake.
A Data Lake collects data from many sources so teams can store, process and analyse it for reporting, machine learning and operational insight. It is useful when formats and future use cases vary, but it needs clear governance to avoid becoming an unmanaged data repository.
A Data Lake usually stores raw or lightly transformed data and supports many data types, while a warehouse typically stores curated, structured data for defined reporting needs. Many companies use both, or adopt a lakehouse approach that combines flexible storage with warehouse-style governance and performance.
A Data Lake specialist may work with Amazon S3, Azure Data Lake Storage, Google Cloud Storage, Apache Spark, Kafka, Airflow, Databricks and Apache Iceberg. They should also bring strong SQL and Python skills, plus knowledge of orchestration, metadata, data quality, identity and access management.
A Data Lake project needs experience that matches its scope, source systems and compliance requirements rather than a fixed number of years. A small ingestion setup may need one focused specialist, while a migration or lakehouse program requires proven architecture, delivery and operating-model skills.
A Data Lake project can often be delivered remotely when cloud access, documentation and decision processes are ready. On-site workshops in Frankfurt may still help with source-system discovery, security reviews and coordination with German-speaking business or infrastructure teams.
A strong Data Lake professional explains storage, processing and governance choices in terms of business needs. Ask for examples of reliable pipelines, data contracts, monitoring, access controls and documentation, and test whether they can describe failure recovery as clearly as the happy path.
A Data Lake engagement often depends on cloud infrastructure, DevOps, security, data modelling, BI and machine learning knowledge. Experience with Kubernetes, Terraform, CI/CD, catalogues and observability is valuable when the platform must be repeatable and operated across teams.
A Data Lake may be excessive when a company only needs a small, stable reporting dataset with simple transformations. It can also fail when ownership, quality rules and access policies are left undefined, so a well-scoped warehouse or managed analytics service may be the better fit.
The average hourly rate of freelancers in Frankfurt, Germany who have used Data Lake in their recent projects is 88 €, which corresponds to a daily rate of about 707 € based on an 8-hour working day.
Of the freelancers in Frankfurt, Germany who have used Data Lake in their recent projects, 100% hold at least a Bachelor's degree, 50% hold at least a Master's degree, and 20% hold a doctorate.
On average, freelancers in Frankfurt, Germany who have used Data Lake in their recent projects have 22 years of professional experience, with a single engagement typically lasting around 2.8 years.
The most common languages among freelancers in Frankfurt, Germany who have used Data Lake in their recent projects are German (100%), English (100%), and French (27%).
The most common industries among freelancers in Frankfurt, Germany who have used Data Lake in their recent projects are Information Technology (82%), Healthcare (73%), and Banking and Finance (55%).
The most common business areas among freelancers in Frankfurt, Germany who have used Data Lake in their recent projects are Information Technology (100%), Business Intelligence (73%), and Project Management (73%).
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.
Berlin 
Hamburg 
Munich Request a free demo
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FRATCH CEO
