
Data Lake Experts
in minutes with precise AI matching and vetted, available freelancersHire experts who design scalable data storage, build ingestion pipelines and connect analytics, machine learning and governance workflows. FRATCH matches you with vetted, available freelancers whose experience fits your Data Lake project quickly and precisely.
Meet FRATCH Experts who have recently used Data Lake
Max R.
Last position:
Senior AI Engineer and Technical Advisor (Freelancer) at dialpad
Serving as AI technical advisor to Dialpad's Telephony Engineering division while building AI products hands-on. Currently developing MCP-based tooling for AI text and voice agents: agent authoring, integrations and adoption of existing agents, shared authentication and API contracts, and durable workflow state. This includes text and voice tests that evaluate agent behavior, latency and task completion.
In parallel, helping Dialpad's engineers adopt QualityLayer and use Claude Code and Codex effectively. This ongoing work covers context management for large codebases, reusable plan-implement-test-review workflows connected to CI/CD, playbooks, hands-on workshops, labs and pair programming.
Technologies: AI Agent Products (Text & Voice Agents, MCP, LLMs, Python, TypeScript), Agent Tooling (Authoring, Integrations, OAuth, Durable State), Quality & Evaluation (Text & Voice Tests, Evals, Regression, Traces), Agentic Engineering (Claude Code, Codex, QualityLayer, Hooks, Prompts), Context Engineering (RAG, Vector Stores, Codebase Embeddings), Software Delivery (CI/CD Integration, Agentic Loops, PR Automation), Enablement & Coaching (Workshops, Hands-On Labs, Pair Programming)
Oliver V.
Last position:
Interim Manager and Management Consultant at Self-employed
- Developed a market entry strategy for the claims division of an insurance services provider.
- Analyzed and optimized existing claims processes.
- Defined management metrics and KPIs to improve performance and efficiency.
- Advised management on market positioning and process digitalization.
- Led an IT team of 13 employees as part of an interim vacancy cover.
- Ensured stable IT operations and managed external IT service providers.
- Led regulatory projects, particularly the implementation of the DORA regulation.
- Prepared and supported an IT security audit.
- Change management and conflict moderation in a challenging transformation environment (FI migration).
Nicole F.
Last position:
Project Manager Datalake and Payment Service Provider at EnBW Energie Baden-Württemberg AG
- Implementation / restructuring of various digital payment use cases
- Project leadership and management, including scheduling, resource planning, and budgeting
- Implementation of posting processes in SAP in the subledgers and processing of account statements for invoice verification
- Definition of future operations and operating processes
- Collaboration with stakeholders to define business requirements and ensure that the project solutions meet them
- Identification and management of project risks and issues, including the development of risk mitigation and problem-solving strategies
- Promoting user acceptance through effective communication, training, and support strategies
- Advising on the practical implementation of various GDPR rulings in IT and business processes
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
Karin A.
Last position:
AI Benchmark Engineer | Native language specialist German at Lilt
- Task Engineering: Evaluating Coding Agents.
- Asset Creation: Building realistic task environments using datasets and files in German. Crucially, these assets must remain in the target language to genuinely measure multilingual handling.
- Prompting & Translation: finding failure points where AI does not work, in German.
- Implementation & Verification: Supporting the development of robust solutions (reference implementations) and write highly reliable, deterministic verifier scripts (using rubric-based judging only when strictly necessary).
- Calibration & Execution: Analyze execution logs and calibrate task difficulty (Easy to Very Hard) using standard Terminal-Bench run configurations against various model tiers (Haiku, Opus).
- Quality Assurance: Participation in a rigorous, 4-layer human quality control process (creation, human review, calibration review, and audit) alongside automated LLM-based checks to ensure fairness, grammatical accuracy, and benchmark integrity.
- Linguistic Review: Reviewing AI benchmark tasks across Hindi, Arabic, Japanese, Chinese, Czech and Turkish.
Florian B.
Last position:
Project Management / PMO Consultant
PMO & Project Organization Blueprint for Restructuring
Renewable Energy / Solar Equipment
Technologies / Methods: PMO setup, KPI tracking, project organization, Jira, Confluence
- Developed measures to improve management control during a restruct...
Niko S.
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.
Christian F.
Last position:
Department Head (Interim) at Municipal utilities and transport company
- Defined and established the areas of responsibility
- Built a governance model for the department and its areas of responsibility
- IT strategy, project management, process management, and quality and sustainability management
- Developed a communications strategy for the group
- Created the IT strategy
- Designed templates, guidelines, and processes for consistent ways of working
- Recorded strategic guidelines and grouped ongoing projects – derived a roadmap for strategic planning
- Reviewed ongoing projects
- Prepared staffing calculations and capacity planning
- Defined job profiles
Stephan F.
Last position:
AI & Data Engineer - Azure Enterprise AI and Knowledge Engineering at IB Schlegel - King
Designed and developed an Azure-based AI and data platform transforming distributed project documents into searchable, structured knowledge for an enterprise AI assistant. Combined incremental document processing, hybrid keyword/vector retrieval, knowledge graph extraction, and conversational AI for Microsoft Teams and Microsoft 365 Copilot. Implemented Python document-processing pipelines for PDF, Word, Outlook and text files; Azure Cosmos DB lifecycle management; Azure AI Search; Azure AI Foundry and Azure OpenAI; identity-aware retrieval with Microsoft Entra ID; project authorization; a TypeScript Microsoft Agents SDK agent; Pulumi infrastructure; resilient scheduling, retry and diagnostic tooling. Technology stack: Python, TypeScript, FastAPI, Pydantic, Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure Cosmos DB, Blob/Table Storage, Container Apps/Jobs, Service Bus, Key Vault, Entra ID, Docker, Pulumi, pytest, Microsoft Teams and Microsoft 365 Copilot.
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.
Philipp G.
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
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
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.
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
Bora D.
Last position:
Software Architect at DZ HYP AG
- Lead architect for an enterprise loan digitisation programme, coordinating 12 engineers across five workstreams and serving as final technical authority on system design.
- Cut critical application response times by 68% (display 17.3s → 5.6s; modification 11.8s → 5.0s) through targeted caching, OData query optimisation and lazy-loading refactoring; further optimisation in progress.
- Own production error triage, prioritisation and resolution across a multi-application portfolio supporting live lending operations.
- Design and implement SAP Fiori applications on SAP UI5, TypeScript and RAP, owning delivery from architecture and code through rollout and production support.
- Established C4 architecture documentation and decision records for the full programme, enabling faster onboarding and consistent cross-workstream design governance.
- Co-managed S/4HANA release cycle alongside primary responsibilities, coordinating directly with SAP support to resolve critical system issues across the portfolio.
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
2.5 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
97%
Master's degree or higher
70%
Doctorate
18%

Certifications per freelancer
4

Most common languages
German, English, French

Speak two or more languages
97%
Based on our profile pool as of 16 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology 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 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 16 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 (86%)
- Professional Services (54%)
- Banking and Finance (47%)
- Automotive (36%)
- Transportation (32%)
- Retail (32%)
- Energy (31%)
- Manufacturing (27%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What a Data Lake is
A Data Lake is a central environment for storing raw structured, semi-structured and unstructured data at scale. It keeps source data in formats close to its origin, so teams can use it later for reporting, advanced analytics, machine learning and operational insights. Unlike a traditional warehouse, it does not require every dataset to follow a fixed schema before ingestion.
What companies build
Companies use Data Lakes to bring together application records, event streams, documents, sensor data and external sources. They support customer analytics, fraud detection, recommendation systems, forecasting, research and large-scale reporting.
- Consolidate data from cloud services, databases and business applications
- Prepare trusted datasets for analytics and machine learning
- Retain raw history for exploration, auditing and future use
- Serve data products to analysts, applications and decision systems
Ecosystem and tooling
Strong specialists understand the storage, compute and orchestration layers around a Data Lake. Common environments include Amazon S3 with AWS Glue and Athena, Azure Data Lake Storage with Microsoft Fabric or Synapse, and Google Cloud Storage with BigQuery and Dataproc. Apache Spark, Apache Kafka, dbt, Airflow, Trino and catalog tools often connect ingestion, transformation and discovery.
When expertise matters
Freelance expertise helps when a lake has grown without clear ownership, reliable pipelines or usable metadata. It is also valuable during cloud migrations, platform consolidation, analytics modernization and the introduction of machine learning workloads.
- Data is duplicated across isolated systems
- Analysts cannot find or trust available datasets
- Pipelines fail silently or lack observability
- Access rules and retention practices are unclear
Delivery and governance
A capable professional defines zones, naming conventions, ownership and lifecycle rules before adding more sources. They establish batch and streaming ingestion, schema evolution, data quality checks, lineage, cataloging and role-based access. They also address encryption, retention, cost control and the risk of creating a data swamp.
Signs of strong specialists
Look for practical experience with the cloud environment, storage formats and workloads your company uses. Strong professionals can explain trade-offs between a Data Lake, data warehouse and lakehouse, then connect architecture decisions to business needs. They test pipelines, document decisions and make failure handling visible rather than relying on informal knowledge.
A good specialist also works across data producers, analysts, security teams and business stakeholders. They leave behind maintainable infrastructure, clear operating procedures and datasets that can be discovered and used with confidence.
Frequently asked questions
Curious about Data Lake? Here are the answers that come up again and again.
A Data Lake stores raw data from many sources so it can support analytics, machine learning, reporting and future use cases. It is useful when a company needs to retain varied data before deciding how every dataset should be structured.
A Data Lake usually stores data in its original or lightly processed form, while a data warehouse organizes curated data for consistent reporting and fast business queries. Many companies use both, with the lake supporting exploration and the warehouse serving governed analytical outputs.
A Data Lake provides flexible, low-cost storage, while a lakehouse adds warehouse-style tables, transactions, governance and query performance on top of that foundation. The choice depends on workload requirements, data quality expectations and the capabilities of the selected cloud stack.
A Data Lake specialist should usually understand SQL, Python, distributed processing, cloud storage, orchestration and data modeling. Experience with Apache Spark, Kafka, dbt, Airflow, catalogs, observability and access control is also valuable when the work spans the full data lifecycle.
A Data Lake project needs a specialist who has handled the relevant ingestion patterns, cloud services and analytical workloads in practice. The right level depends on whether the assignment covers a focused pipeline, a migration, or architecture and governance across the company.
A Data Lake assignment can often be delivered remotely when documentation, access procedures and communication are well organized. On-site sessions can still help with workshops, legacy-system discovery and alignment between data owners, security teams and business stakeholders.
A Data Lake professional should explain storage, compute, governance and cost decisions in terms the wider team can understand. Review examples of pipeline testing, lineage, monitoring, access design and documentation, not only diagrams of the target architecture.
A Data Lake becomes difficult to use when ownership, metadata, quality checks and access rules are missing. Unclear naming, duplicated sources, unreliable pipelines and uncontrolled retention can turn flexible storage into a data swamp.
The average hourly rate of freelancers who have used Data Lake in their recent projects is 103 €, which corresponds to a daily rate of about 826 € based on an 8-hour working day.
Of the freelancers who have used Data Lake in their recent projects, 97% hold at least a Bachelor's degree, 70% hold at least a Master's degree, and 18% 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 2.5 years.
The most common languages among freelancers who have used Data Lake in their recent projects are German (98%), English (97%), and French (19%).
The most common industries among freelancers who have used Data Lake in their recent projects are Information Technology (86%), Professional Services (54%), and Banking and Finance (47%).
The most common business areas among freelancers who have used Data Lake in their recent projects are Information Technology (96%), Business Intelligence (78%), and Product Development (67%).
Main locations of FRATCH Experts, who have recently used Data Lake
Our freelancers and interim experts are at home all over Germany — available on-site in Berlin, Hamburg, Munich and every major business hub, or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.
Across Switzerland our specialists are active in Zurich, Geneva, Basel and Bern — working on-site or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.
Countries:
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