Data Pipeline Expert
to move trusted data faster with AI-matched, vetted freelancersHire experts who design reliable ingestion flows, orchestrate batch and streaming workloads, and connect cloud data warehouses with operational systems. FRATCH finds a precise match quickly from vetted, available freelancers.
Meet FRATCH Experts who have recently used Data Pipeline
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)
Fadi S.
Last position:
AI-Engineer at Recruitment Agency
- Design, development, and productive rollout of an Enterprise HR Copilot based on Microsoft Copilot Studio to automate internal HR processes
- Development of an AI-supported assistant for answering employee inquiries based on a SharePoint knowledge base and internal company HR documentation
- Integration of the Copilot into Microsoft Teams, including welcome logic, user guidance, and role-based routing
- Automation of HR processes and approval workflows with Microsoft Power Automate to reduce manual processing steps
- Development of realistic HR documents and response templates with Azure OpenAI, taking business requirements and consistent company policies into account
- Implementation of prompt strategies, knowledge management, and response optimization to improve response quality and user acceptance
- Close collaboration with business departments to analyze requirements and integrate the AI solution into existing company processes
Technologies:
Microsoft Copilot Studio, Microsoft Teams, Azure OpenAI, SharePoint, Power Automate, Power Platform, Microsoft 365, Prompt Engineering, Workflow Automation
Jens H.
Last position:
Interim CTO (occasional assignments) at Fujitsu / FSAS
Stabilization of an Azure/.NET landscape in live operation.
- Architecture, DevOps, and operational readiness; technical decisions under time pressure
- Azure DevOps, monitoring, ETL/ELT, cloud security, FinOps, and data-mesh-related topics
Technologies: Azure DevOps, .NET, CI/CD, monitoring, FinOps
Chintan P.
Last position:
Product Owner and Technical Product Lead at Sustamize GmbH
LLM-based features for automated CO₂e data extraction from unstructured documents (70% reduction)
Agentic AI pipeline for automated Scope 3 emissions calculations with 150.000+ validated data records
Intelligent API workflows for real-time carbon footprint calculations in ERP and ESG systems
ML algorithms for predicting emissions hotspots and optimizing product design
Automated data validation pipelines with NLP for quality assurance of CO₂e datasets
Led a 15-person cross-functional team in developing 10+ AI features
Strategic product planning and AI roadmap with 35% shorter time-to-market
Stakeholder management with DAX companies (40% higher satisfaction, 95% retention)
On-time project delivery with 95% budget adherence through data-driven backlog management
Agile methods (Scrum, Kanban) with continuous AI/ML integration (25% increase in team velocity)
Product-market fit for AI features through A/B testing and analytics (60% higher adoption rate)
Dmitry P.
Last position:
Freelance Digital Marketing Analyst at Freelance
- Marketing Strategy: Lead the end-to-end analysis and evaluation of cross-channel marketing campaigns across the entire Customer Journey. My focus is identifying optimization potential and deriving clear, actionable recommendations that drive measurable business impact.
- Data Science & AI: Advanced predictive modeling (Churn, LTV), market basket analysis, clustering, and real-time AI-powered audience discovery utilizing RAG/LLMs.
- Marketing Analytics & Measurement: End-to-end attribution analysis, Marketing Mix Modeling (MMM), audience segmentation, conversion path analysis, and A/B testing across all major platforms.
- Data Engineering & Reporting: Designing and managing robust, multi-platform data pipelines (BigQuery, GCP) for data consolidation, automated dashboard generation, and critical API integrations.
Michael N.
Last position:
Senior AI Engineer | Forward Deployed Engineer at Tiefbau
- Development of an AI-powered project organization tool for a civil engineering company that intelligently links project, task, tender, schedule, and document data through a knowledge graph.
- Implementation of AI features for document analysis, information extraction, context-based assistance, and voice-based data capture based on Microsoft Azure AI, reducing administrative effort, making information available faster, and supporting project teams in decision-making.
- Tech stack: Python, React, TypeScript, FastAPI, Claude Code, Codex, Graphify, PostgreSQL, Microsoft Azure AI Foundry, Azure OpenAI, Azure AI Speech, Azure AI Document Intelligence, Microsoft Graph, Microsoft Entra ID, Docker, Git, CI/CD.
Deepa K.
Last position:
Data Analyst – BI Lead Engineer at Novartis
- Leading enterprise BI transformation across Power BI & Microsoft Fabric, delivering scalable data models, automated reporting, and high-performance analytics solutions for commercial and operational leadership.
- Building and optimizing Power BI Dataflows, Fabric Lakehouse datasets, semantic models, and automated reporting pipelines to improve data scalability, governance, and reporting performance.
- Driving dashboard modernization and KPI governance by translating complex business requirements into executive-level insights, interactive visualizations, and decision-ready analytics.
- Designing end-to-end Microsoft Fabric architectures integrating data ingestion, transformation, virtualization, and enterprise reporting across cross-functional business domains with SAP BW to Qlik to Power BI migration.
- Delivering AI-enabled reporting capabilities, threshold-based alerting, and automation frameworks within the Power BI ecosystem to accelerate business decision-making.
- Partnering with commercial leadership, analytics teams, and IT stakeholders to standardize KPIs, optimize BI strategy, and deliver scalable, business-critical reporting solutions.
- Recognized for combining strong stakeholder leadership, technical architecture expertise, and business-driven analytics to deliver impactful enterprise BI transformation initiatives.
Ramazan C.
Last position:
Fullstack-/DevOps Engineer at BKA (Federal Criminal Police Office)
Development and further development of an internal platform for managing and providing technical resources, virtual machines, and infrastructure services. The platform supports self-service processes and covers functions that are conceptually comparable to cloud management solutions like Azure or AWS.
- Responsible involvement in the design, development, and implementation of new backend and frontend features
- Hands-on development with Java, Spring Boot, Python, and Angular
- Implementation of REST interfaces, business logic, validations, and integrations into existing system landscapes
- Further development of modern web interfaces with Angular, including connection to backend services
- Participation in architecture and design decisions within the team, especially with regard to scalability, maintainability, and clean interfaces
- Containerization and deployment of applications with Docker, Kubernetes, and Helm
- Support with CI/CD processes and deployment to Kubernetes-based environments
- Work in the environment of vSphere, Broadcom, GitLab CI/CD, ArgoCD, Maven, npm, and NuGet
- Close collaboration with developers, business teams, DevOps, and other technical stakeholders
- Analysis of technical requirements, deriving suitable solutions, and independent implementation in an agile team
- Use of GitHub Copilot to support code generation, refactoring, test case creation, and technical documentation
Methods/ tools/ technologies: Languages & frameworks: Java (21), Spring Boot (4.x), Python, Angular, Robot Framework, Kubernetes, Helm Persistence: PostgreSQL, MongoDB, Hibernate, Liquibase Architecture & communication: REST, gRPC, GraphQL, Apache Kafka, OpenAPI, Microservices, Event Driven, Domain Driven Design Cloud & infrastructure: Terraform, Docker, Rancher, Helm, Ansible Security: OAuth2, MS (Entra ID), web security, Keycloak (extensions for detailed group rights) DevOps: GitLab CI/CD, Ansible, Maven, Gradle, Grafana, Prometheus, Git, GitHub Copilot Testing & QM: JUnit, Robot Framework, automated component and integration tests, E2E tests with Playwright, Testcontainers, EasyMock Methodology & approach: Kanban, JIRA, Confluence, Clean Code
Sarvesh L.
Last position:
Data Analytics for Renewable Energy Integration at Harz University
- Created data pipelines and visualization tools to support sustainable energy decision-making
- Developed insights that could optimize renewable energy deployment and grid integration strategies
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.
Matthias S.
Last position:
Software Developer and Consultant at CLADE GmbH
- Analysis of the existing CAN communication between microcontrollers
- Analysis of the sensors used and the measured values collected
- Planning the CAN messages for transmitting the measured values
- Iterative adjustment of the microcontroller code to the new CAN messages
- Cross-compilation from x64 to arm64
Dennis O.
Last position:
Co-Founder & CEO at Connect AI
AI Agent SaaS
Founded and built an AI agent SaaS for automated customer support and lead qualification, including implementation with pilot customers.
- Service → product transformation: productization from the agency into an independent AI agent SaaS. The starting point was a specific customer problem; the product was developed together with the customer.
- Developed and launched AI agents for website inbound, lead qualification, and customer service, reaching €8K MRR.
- Structured and led a remote team across engineering, LLM ops, and customer enablement.
- Case PTC Auto (e-commerce on Shopify): connected an AI support agent to the store, automated product data & support workflows, reducing workload by ~10–15 hours per week per support employee.
- Set up GDPR-compliant data processing for the AI agents: data processing agreements, deletion policies, and customer data storage locations.
- Assessed agents against the EU AI Act (risk class, transparency requirements) and built them accordingly.
- Addressed customers’ compliance and security requirements (questionnaires, evidence, contracts) to make sales possible in the first place.
- Built technical safeguards into the product to prevent agent misbehavior: hallucinations, escalation to humans, and logging.
Martin H.
Last position:
Lead Product Owner at Energy
- Team leadership: Prioritization and coordination of four cross-functional teams.
- Platform strategy: Development and implementation of strategies to optimize existing IT platforms.
- Stakeholder management: Active management of expectations and communication with internal and external stakeholders.
- Program and innovation management: Prioritization and coordination of cross-department projects as well as innovation initiatives.
- Product Owner consulting: Advising Product Owners with a focus on product development and continuous product improvement.
- Organizational development: Improving communication and decision-making structures across all organizational levels.
- Change management: Implementing best-practice change management methods to ensure continuous optimization and innovation.
- Quality assurance: Ensuring high quality standards in processes, services, and deliverables.
Asım Y.
Last position:
Freelance BI/DWH/ETL Specialist at Akbank
- Setting architectural standards for Informatica Power Center development teams
- Maintenance-performance-health for DWH (Exadata) environments
- Qlik Sense / BO reports architectural standards
- Implementing best practices in ETL tasks
- Documentation ETL standards on Confluence
- Dynamic Data Masking rules for GDPR/KVKK regulations by using Informatica DDM tool
- DWH Data model Power Designer
- Upgrade Qlik Sense versions
- Merge all Informatica server's repository databases on one Oracle DB
Varsha P.
Last position:
Senior Data Analyst at Infosys
Enterprise Analytics Modernization – Germany-based enterprise reporting platform for operations and management analytics, used by 1,000+ internal users across multiple departments.
- Lead end-to-end Power BI and Microsoft Fabric reporting initiatives, delivering scalable dashboards and semantic models supporting daily operational and strategic decisions, achieving 30% faster decision turnaround and 25% reporting efficiency gains.
- Designed unified enterprise datasets using Microsoft Fabric Lakehouse and OneLake, automating historical data processing and reducing manual reporting effort by 40%.
- Built and maintained automated ingestion pipelines using Fabric Dataflows Gen2 and Data Pipelines, improving data refresh reliability to 99.8% uptime and ensuring consistent data quality.
- Implemented enterprise reporting governance, including Row-Level Security (RLS), workspace strategy, deployment pipelines, and documentation, increasing dashboard adoption by 35%.
Technologies used: Power BI, Microsoft Fabric, DAX, Power Query, SQL, Azure Data Fundamentals, Semantic Modeling, RLS, Agile
Discover over 15,000 top freelancers
Statistics of experts using Data Pipeline
Aggregated from the professional profiles of matched freelancers.
Experience
13 years
Position duration
2.8 years
Positions per freelancer
8
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Banking and Finance, Education
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
98%
Master's degree or higher
72%
Doctorate
13%
Certifications per freelancer
3
Most common languages
English, German, French
Speak two or more languages
98%
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 Pipeline
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 Pipeline 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 (84%)
- Banking and Finance (39%)
- Education (36%)
- Automotive (36%)
- Professional Services (35%)
- Retail (30%)
- Healthcare (28%)
- Manufacturing (28%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Core purpose
A data pipeline moves information from source systems to destinations where teams can analyze it, operate on it, or use it in products. It can collect application events, transactions, files, sensor readings, or third-party data, then validate, transform, enrich, and deliver that information. Good pipeline design makes data timely, traceable, and usable without creating fragile dependencies.
Pipeline patterns
Batch pipelines process data on a schedule and suit reporting, billing, and large historical loads. Streaming pipelines handle events as they arrive and support monitoring, personalization, fraud detection, and operational alerts. Many companies combine both patterns, using change data capture or event queues for low-latency updates and scheduled jobs for reconciliation and backfills.
Ecosystem and tooling
Specialists work across ingestion, storage, transformation, orchestration, and observability. Common components include Apache Kafka, Apache Airflow, dbt, Spark, Flink, cloud object storage, and warehouses such as Snowflake, BigQuery, or Redshift. They also use Python, SQL, APIs, message brokers, schema registries, CI/CD, secrets management, and infrastructure-as-code.
When expertise matters
Companies often bring in freelance specialists when data sources multiply, delivery delays become visible, or an existing flow is difficult to change safely.
- Replacing manual exports with dependable ingestion
- Migrating pipelines between cloud or warehouse environments
- Adding event streaming, CDC, or real-time processing
- Repairing failed jobs, duplicate records, and late-arriving data
- Establishing lineage, tests, alerts, and recovery procedures
Quality signals
Strong professionals define clear contracts for schemas, ownership, freshness, and acceptable failure. They make pipelines idempotent where possible, handle retries and late data deliberately, and separate raw, transformed, and serving layers. They also measure throughput, latency, cost, and data quality instead of treating a successful job run as proof that the system works.
Project collaboration
A pipeline engagement works best when the expert can inspect source systems, clarify business meaning, and document decisions for the internal team. Remote work is effective when access, environments, and ownership are prepared; on-site collaboration can help when legacy systems, restricted networks, or cross-functional process changes are involved. Useful deliverables include architecture diagrams, infrastructure definitions, tested transformations, runbooks, dashboards, and handover sessions.
Frequently asked questions
Everything clients usually want to know about Data Pipeline, in one place.
A Data Pipeline transfers data between systems and applies controlled processing along the way. Companies use pipelines to feed analytics warehouses, machine learning datasets, dashboards, business applications, and real-time monitoring.
Data Pipeline is the broader term for moving and processing data, while ETL describes extracting, transforming, and then loading it. ELT loads data before transformation, which is common with modern cloud warehouses; a pipeline may use either pattern or combine them with streaming and batch processing.
A strong Data Pipeline specialist usually brings SQL, Python, data modeling, orchestration, cloud storage, and warehouse experience. Knowledge of Kafka or another event system, CI/CD, infrastructure-as-code, observability, security, and data governance is also valuable.
The right level depends on the system’s risk and scope, not a fixed number of years. A smaller ingestion flow may need focused implementation skills, while a regulated or high-volume environment requires someone who can design recovery, lineage, access controls, testing, and operational ownership.
Yes, Data Pipeline work is often suitable for remote collaboration because design, coding, testing, and documentation happen in shared environments. Access to source systems, clear data ownership, secure credentials, and agreed review routines are essential; on-site work may help with restricted infrastructure or complex legacy systems.
Ask the specialist to explain how the Data Pipeline handles schema changes, duplicates, retries, late data, backfills, and partial failures. Review tests, monitoring, lineage, documentation, deployment practices, and recovery procedures rather than judging quality only by whether a scheduled job completes.
Bring in a Data Pipeline freelancer when manual transfers, unreliable jobs, growing data volume, or new streaming requirements slow the business. External expertise is also useful for cloud migrations, warehouse redesigns, event-driven products, and a focused review of an inherited pipeline.
Before starting Data Pipeline work, clarify source ownership, data definitions, delivery expectations, security boundaries, retention, failure handling, and who operates the result. Confirm the target architecture, deployment process, access model, test data, and handover responsibilities so technical work supports a real business use case.
The average hourly rate of freelancers who have used Data Pipeline in their recent projects is 87 €, which corresponds to a daily rate of about 699 € based on an 8-hour working day.
Of the freelancers who have used Data Pipeline in their recent projects, 98% hold at least a Bachelor's degree, 72% hold at least a Master's degree, and 13% hold a doctorate.
On average, freelancers who have used Data Pipeline in their recent projects have 13 years of professional experience, with a single engagement typically lasting around 2.8 years.
The most common languages among freelancers who have used Data Pipeline in their recent projects are English (99%), German (98%), and French (14%).
The most common industries among freelancers who have used Data Pipeline in their recent projects are Information Technology (84%), Banking and Finance (39%), and Education (36%).
The most common business areas among freelancers who have used Data Pipeline in their recent projects are Information Technology (96%), Business Intelligence (78%), and Product Development (75%).
Main locations of FRATCH Experts, who have recently used Data Pipeline
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.
Berlin 
Hamburg 
Munich 
Cologne 
Frankfurt 
Dusseldorf 
Essen 
Nuremberg In Austria our freelancers and interim experts support companies from Vienna to Graz — on-site where your project needs them, or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.
Vienna Graz 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.
Zurich Geneva Basel Bern Countries:
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Philipp Thomaschewski
FRATCH CEO
