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Data Lake Experts

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Hire experts who design data lake storage, build ingestion and transformation pipelines, and connect raw data to analytics and machine learning. Get fast, precise matching with vetted, available freelancers.

Meet FRATCH Experts who have recently used Data Lake

Verified expert

Niko Schmuck

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Developing Architect / Solution Architect

Hamburg
Niko Schmuck

Last position:

Developing Architect, Technical Lead "gridlytics" at HH Energienetze

  • Building a data integration platform for high, medium, and low voltage assets for contextual analysis of time series with master data from the SCADA control system (IEC 60870 104), INIS, and SAP.
  • Responsibility for the architecture and implementation of the solution, as well as sparring partner for the Product Owner.
  • Use of Kotlin, Spring Boot, Maven, TimescaleDB, PostgreSQL, liquibase, Elements IoT, Docker, Kubernetes, Grafana, Python, jupyter, and various API gateways.
Verified expert

Umut Gülac

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Freelancer

Frankfurt
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.
Verified expert

Christian Frauer

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IT Project Implementer (Problem Solver)

Buxtehude
Christian Frauer

Last position:

Department Head (Interim) at Municipal utility and transport company

  • Definition and setup of the subject areas
  • Building a governance model for the department with the areas of responsibility
  • IT strategy, project management, process management, and quality and sustainability management
  • Developing a communication strategy for the group
  • Creating an IT strategy
  • Designing templates, guidelines, and processes for standardized work
  • Capturing strategic guardrails and grouping ongoing projects – deriving a roadmap for strategic planning
  • Reviewing ongoing projects
  • Creating staffing and capacity calculations
  • Defining job profiles
Verified expert

Florian B.

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Program & Integration Lead (AI, Data & Analytics Transformation)

Florian B.

Last position:

Business Architect — Project Organization Blueprint for Restructuring

Tasks & results:

  • Developed measures to improve management control during a restructuring program (approx. 80 people involved)
  • Set up the PMO to enforce transparency, reporting, and data-driven decisions
  • Developed an integration template to move team silos (software, field installation, supply chain) into a cross-functional project structure with lean tracking systems for timeline, progress, and KPIs
  • Technologies / methods: PMO setup, KPI tracking, project organization, Jira, Confluence
Verified expert

Alexander Zhirov

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Senior Data Architect & Data Engineer

Berlin
Alexander Zhirov

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.
Verified expert

Philipp Grunert

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Machine Learning & Data Engineer

München
Philipp Grunert

Last position:

Data Scientist & ML Engineer at Data-Science Factory GmbH

  • Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
  • Implementation of automated end-to-end cloud processes
  • Development of LLM and NLP models
  • Creation of interactive reports
  • Support for national and international large corporations as well as medium-sized companies in implementing ML projects
Verified expert

Justina Kmiecik

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Data Management & Governance Manager

Oberursel
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.
Verified expert

Alexander Bromberg

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Senior Data Engineer

Köln
Alexander Bromberg

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

Verified expert

Samuel Kopp

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Agentic AI Engineer & Technical Lead

Ingolstadt
Samuel Kopp

Last position:

Founder & Agentic AI Engineer at Agentakt LLC

Independent engineering practice focused on custom AI systems, production delivery, and fractional technical leadership.

Selected client engagement: Scalutions

  • Role: Serve as fractional CTO and hands-on technical lead, responsible for the architecture and agentic infrastructure behind its managed B2B outbound operation.

  • Product: Designed and built OutboundLoop, an agentic SDR operating system for research, qualification, personalized outreach, campaign management, human approvals, measurement, and continuous improvement.

  • Scope: Own the full system lifecycle—from business processes and agent behavior to context design, model routing, integrations, evaluation, telemetry, reliability, cost control, and production operations.

Verified expert

Prasad Tilloo

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Solution Architect / Senior Manager – DTC E-Commerce Platform

Frankfurt
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
Verified expert

Nisanthan Sivarajah

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BI Consultant

Berlin
Nisanthan Sivarajah

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.

Verified expert

Oliver Voss

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Interim Manager and Business Consultant

Hamburg
Oliver Voss

Last position:

Interim Manager and Business Consultant at Self-employed

  • Development of an entry strategy for the claims area of an insurance service provider.
  • Analysis and optimization of existing claims processes.
  • Definition of management metrics and KPIs to improve performance and efficiency.
  • Advising management on market positioning and process digitalization.
  • Leading an IT team with 13 employees in a temporary vacancy role.
  • Ensuring stable IT operations and managing external IT service providers.
  • Leading regulatory projects, especially for the implementation of the DORA regulation.
  • Preparation for and support during an IT security audit.
  • Change management and conflict moderation in a challenging transformation environment (FI migration).
Verified expert

Jorge Machado

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Data Expert

Würzburg
Jorge Machado

Last position:

Technical Lead / Fractional CTO at Würth GmbH

I designed and developed an AI-powered multi-tenant platform on Azure that transforms SAP process recordings into technical documentation, presentations and automated tests, processing over 15,000 process recordings for enterprise customers like Würth. I owned the architecture, the production releases and the DevOps setup. I also designed a multi-tenant system with SSO and role-based access on Azure. Implemented an MCP Server with Dynamic OAuth Authentication.

Main Tasks:

  • Sprint planning and feature preparation
  • Design the multi-tenant platform architecture (FastAPI, SQLAlchemy, PostgreSQL row-level security for tenant isolation)
  • Develop AI pipelines with Prefect for transcription (Azure Speech API), document generation and SAP screen-recording analysis (Claude, gpt-4-mini)
  • Design and implement an MCP server to expose tenant knowledge to LLM clients (Claude), with async retrieval and reranking
  • Implement LLM cost tracking, rate limiting and client pooling for Anthropic/OpenAI/Azure OpenAI endpoints
  • Set up CI/CD: Docker images to Azure Container Registry, GitHub Actions, Azure Static Web Apps, Alembic migrations in containers
  • Manage production releases and execute live data migrations for enterprise customers
  • Define engineering standards and architecture patterns for the team

Environment: Azure / Azure Foundry / Python / FastAPI / Prefect / React / PostgreSQL

Verified expert

Lino Giefer

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Senior Machine Learning Engineer

Scharbeutz
Lino Giefer

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)

Discover over 15,000 top freelancers

Statistics of experts using Data Lake

Aggregated from the professional profiles of matched freelancers.

Experience

18 years

Position duration

2.4 years

Positions per freelancer

10

Top business areas

Information Technology, Business Intelligence, Product Development

Top industries

Information Technology, Professional Services, Banking and Finance

Certification focus areas

Information Technology, Business Intelligence, Project Management

Bachelor's degree or higher

95%

Master's degree or higher

70%

Doctorate

19%

Certifications per freelancer

4

Most common languages

German, English, French

Speak two or more languages

97%

Based on our profile pool as of 6 Sep 2026.

Daily rate distribution

0 20 40 60 80
<€400 €400-​800 €800-​1200 €1200+

The chart shows how the daily rates of freelancers in this technology 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 using Data Lake

Rates are based on recent contracts and do not include FRATCH margin.

1000
750
500
250
Rate comparison chart
Daily rate avg. 832 €

The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.

1000
750
500
250
Rate comparison chart
Median rate 800 €

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 6 Sep 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 is a central place to store raw and processed data in its original form. It is used for analytics, reporting, machine learning, and data sharing across teams. Strong work with a data lake starts with clear data zones, file formats, and ownership.

Common stack

  • Object storage such as Amazon S3, Azure Data Lake Storage, or Google Cloud Storage
  • Table formats and query layers such as Delta Lake, Apache Iceberg, or Apache Hudi
  • Processing with Spark, SQL engines, or streaming tools
  • Catalogs, lineage, and access control for governance

Where it helps

Companies bring in data lake specialists when data is scattered across systems and teams need one place to work from. It is a common fit for finance, retail, media, healthcare, and industrial data. In Germany, many projects also need experts who can work well with local teams and English-speaking data groups.

Typical deliverables

A freelance specialist can design the lake structure, set up ingestion jobs, improve partitioning, and clean up tables for faster queries. They also document data contracts, retention rules, and access policies. Good deliverables are practical: pipelines that run reliably and data that analysts can trust.

What strong specialists do

Strong professionals know how to balance flexibility with control. They avoid a dumping ground and build a usable layer for analytics and downstream products. They understand schema evolution, metadata, cost control, and how to keep batch and streaming data consistent.

Choosing the right fit

When you review candidates, look for experience with real data lake problems, not just storage setup. Ask how they handled late-arriving data, duplicate records, and slow queries. The best experts can explain trade-offs clearly and adapt the design to your cloud stack and delivery process.

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Frequently asked questions

Curious about Data Lake? Here are the answers that come up again and again.

A data lake stores raw and curated data for analytics, dashboards, machine learning, and ad hoc exploration. Companies use it when they want one place for many source systems instead of separate silos. It works best when ingestion, governance, and query access are designed together.

A data lake usually keeps data in more flexible, less structured form, while a data warehouse is optimized for curated reporting and strict schemas. Many teams use both: the lake for landing and preparation, the warehouse for business-facing reports. The right choice depends on who consumes the data and how fast it must change.

The most common term is data lake, but you will also see the full cloud vendor names around it, such as Amazon S3-based lakes or Azure Data Lake Storage. In modern stack discussions, people often mention Lakehouse when they want lake storage with warehouse-style table management. Good specialists should understand those terms and how they relate.

A strong data lake specialist knows SQL, cloud storage, ETL or ELT design, and data modeling for analytics. They should also understand table formats like Delta Lake, Iceberg, or Hudi, plus orchestration, lineage, and access control. Experience with Spark or streaming tools is often important when fresh and historical data need to meet in one place.

A data lake project can need anything from focused support to deep architecture work, depending on the current state of your data. If you already have cloud storage and just need better pipelines or governance, a specialist with practical implementation experience may be enough. If the lake is meant to become a core analytics layer, you need someone who has solved reliability, cost, and quality issues before.

Yes, Data Lake work is often done remotely because most tasks live in cloud consoles, notebooks, and code repositories. On-site time helps when the project needs workshops with analysts, security teams, or domain experts. For teams in Germany, remote collaboration usually works well if the specialist communicates clearly and documents decisions.

A data lake candidate should be able to explain how they prevent messy data from spreading through the stack. Look for concrete answers about partitioning, schema evolution, retries, monitoring, and data quality checks. Good specialists talk about outcomes, not just tools.

A data lake project often includes cloud services, orchestration tools, SQL engines, and governance catalogs. Many specialists also work with streaming systems, dbt-style transformations, or notebooks for analysis and validation. If your project touches machine learning, feature pipelines are another common adjacent area.

The average hourly rate of freelancers who have used Data Lake in their recent projects is 104 €, which corresponds to a daily rate of about 832 € based on an 8-hour working day.

Of the freelancers who have used Data Lake in their recent projects, 95% hold at least a Bachelor's degree, 70% hold at least a Master's degree, and 19% hold a doctorate.

On average, freelancers who have used Data Lake in their recent projects have 18 years of professional experience, with a single engagement typically lasting around 2.4 years.

The most common languages among freelancers who have used Data Lake in their recent projects are German (97%), English (97%), and French (20%).

The most common industries among freelancers who have used Data Lake in their recent projects are Information Technology (86%), Professional Services (55%), and Banking and Finance (48%).

The most common business areas among freelancers who have used Data Lake in their recent projects are Information Technology (96%), Business Intelligence (77%), and Product Development (67%).

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 Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg

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FRATCH CEO

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