Data Lake Experts in Frankfurt
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Meet FRATCH Experts in Frankfurt, who have recently used Data Lake
Umut Gülac
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.
Justina Kmiecik
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.
Prasad Tilloo
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
Ulm Paunel
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 Zadeh
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 Krivtsov
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 Kisalita
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 Solanki
Last position:
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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 Ammanath
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 Schaaf
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
21 years (Germany: 18 years)
Position duration
2.6 years (Germany: 2.4 years)
Positions per freelancer
12 (Germany: 10)
Top business areas
Information Technology, Business Intelligence, Project Management
Top industries
Healthcare, Information Technology, Professional Services
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
100% (Germany: 95%)
Master's degree or higher
56% (Germany: 70%)
Doctorate
22% (Germany: 17%)
Certifications per freelancer
4
Most common languages
German, English, French
Speak two or more languages
100% (Germany: 98%)
Based on our profile pool as of 30 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Frankfurt 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 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What it is
A data lake stores raw and curated data in one place so teams can explore, process, and reuse it without forcing a fixed schema first. It is used for reporting, machine learning, streaming data, and central analytics across business units. Strong work here keeps access simple while the data stays organized.
Common stacks
- AWS S3, Lake Formation, Glue, Athena
- Microsoft Azure Data Lake Storage, Synapse, Databricks
- Apache Spark, Trino, Airflow, dbt
- Delta Lake, Apache Iceberg, Apache Hudi
- Kafka and other ingestion tools for batch and streaming
Typical work
A data lake specialist turns scattered sources into reliable pipelines and usable datasets. That often includes ingestion design, partitioning, metadata management, access control, and quality checks. In Frankfurt, this work often supports finance, logistics, trading, and regulated reporting teams that need controlled data access.
When to hire
Companies bring in freelance help when a lake is growing fast, costs are unclear, or teams cannot find the right structure. It also helps during cloud migrations, lakehouse transitions, and cleanup after inconsistent ingestion. If dashboards are slow, tables are messy, or users do not trust the data, the setup needs attention.
What good experts do
A strong professional understands storage, compute, governance, and query patterns together. They know when to keep data raw, when to curate it, and how to separate zones for reliable use.
- Design clear bronze, silver, and gold layers
- Set data quality and schema handling rules
- Optimize files, partitions, and query performance
- Document ownership, lineage, and access policies
Signs you need help
If every team stores data differently, analytics breaks down. If the lake has become a data swamp, the problem is usually not the tool but the structure around it. A freelance expert can quickly review the current setup, identify weak points, and bring order to the platform without a long internal ramp-up.
Frequently asked questions
Before you brief your next project: the most common questions about Data Lake.
A Data Lake is used to store raw and prepared data together so teams can analyze it later without locking it into one model too early. It supports BI, machine learning, streaming analytics, and long-term history. Good setups keep the data flexible but still searchable and governed.
A data lake usually keeps data in its original or lightly processed form, while a warehouse is structured for fast reporting and fixed business models. The lake is better for broad exploration and varied data types. Many teams now use a lakehouse approach when they want lake flexibility with warehouse-like reliability.
You should hire a data lake specialist when ingestion is messy, access rules are unclear, or performance is getting worse as data grows. Freelance help is also useful for cloud migration, governance cleanup, and introducing tools like Delta Lake or Apache Iceberg. It is a good fit when you need focused expertise for a defined phase.
A strong data lake professional usually knows object storage, ETL and ELT patterns, orchestration, SQL query engines, and governance tools. Common stacks include AWS S3, Azure Data Lake Storage, Spark, Airflow, Glue, and Databricks. The right mix depends on whether the lake is batch-heavy, streaming-heavy, or part of a lakehouse design.
Most data lake work can be done remotely if access, security, and communication are set up well. On-site time in Frankfurt can help during discovery, stakeholder workshops, or access reviews in regulated environments. Many companies use a hybrid setup: remote delivery with a few in-person sessions at key milestones.
A good data lake expert leaves behind clear zones, reliable pipelines, and datasets that people trust. Look for practical proof: governance decisions, file and partition strategy, lineage, quality checks, and cost awareness. Strong work is visible in faster queries, fewer broken dashboards, and less time spent fixing data issues.
Not exactly. A data lake is the storage and processing foundation, while a lakehouse adds warehouse-style management, transactional tables, and stronger analytics behavior on top. If your team needs both flexibility and reliable BI, a lakehouse with Delta Lake, Iceberg, or Hudi may be the better target.
Before starting on a data lake project, freelancers should ask where the data comes from, who owns it, what quality rules exist, and which users depend on it. They also need to know the cloud, security model, and whether the goal is a new build or a cleanup. Those details shape the architecture and the delivery plan.
The average hourly rate of freelancers in Frankfurt, Germany who have used Data Lake in their recent projects is 90 €, which corresponds to a daily rate of about 717 € 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, 56% hold at least a Master's degree, and 22% hold a doctorate.
On average, freelancers in Frankfurt, Germany who have used Data Lake in their recent projects have 21 years of professional experience, with a single engagement typically lasting around 2.6 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 (20%).
The most common industries among freelancers in Frankfurt, Germany who have used Data Lake in their recent projects are Healthcare (80%), Information Technology (80%), and Professional Services (60%).
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 (70%), and Project Management (70%).
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
Get in touch with the FRATCH team and we will get back to you within 4 hours.
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We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
