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
Shamaila M.
Last position:
Founder/Kubernetes and Cloud Architect at Kubekanvas
- Developed browser based platform for kubernetes no-code deployment and cluster management
- Developed CLI in Typescript to deploy the resources in the cluster without leaving the browser UI.
- Implemented DevSecOps pipelines: image scanning, SBOM, policy enforcement, supply-chain security and used Kyverno. Implemented IAM integration for the command line utility tool.
- Designed role & permission models for Keycloak, oAuth/OIDC, social login flows.
- Used LLM to convert user intent into the diagram.
- Worked on integration with multiple Sovereign Cloud like stackIt, Hetzner, CIVO, UpCloud plus public clouds like AWS, GCP, Azure
- The technology stack includes Java, SpringBoot, Kubernetes, OpenAI, Kubernetes multi-tenancy using vCluster, Karpenter, RBAC for CLI, Helm, React
Fadi S.
Last position:
Data Scientist at E-Commerce
- Designed and developed a production-ready data platform based on Microsoft Fabric for an e-commerce company
- Built a scalable Medallion Architecture (Bronze, Silver, Gold) with Lakehouse and Delta Lake as the foundation for analytics and AI applications
- Developed a metadata-driven ETL platform with PySpark and a generic Incremental Loading Framework (SQL Server → Bronze → Silver → Gold)
- Developed reusable components for Microsoft Fabric Notebooks to standardize data integration, transformation, and validation
- Implemented automated data pipelines including monitoring, logging, error handling, and Delta MERGE strategies for stable production operations
- Built a business layer architecture for reporting, data warehousing, and reusable data products
- Integrated heterogeneous data sources via SQL Server, REST APIs, Amazon SP API, and Google Analytics 4
- Worked closely with business departments to analyze business requirements and translate them into scalable data architectures
Technologies: Microsoft Fabric, PySpark, Python, SQL Server, Delta Lake, Lakehouse, OneLake, Data Factory, Data Warehouse, REST APIs, Amazon SP-API, Google Analytics 4
Vili D.
Last position:
Technical Lead, Data Engineer at Mercedes-Benz Consulting
Optimized the data architecture (Medallion) to better decouple processing steps and improve transparency and reproducibility
Ensured the technical quality of data processing in Databricks by introducing schema enforcement, data quality checks, and a structured data architecture
Pipeline orchestration with Azure Data Factory
Improved and automated the development and deployment process by integrating Git and GitHub Actions
Technical leadership of the Data Engineering team (3 employees)
Led workshops to optimize and stabilize the data platform and development process
Collected and prioritized new requests and maintained the product backlog
Technologies: Microsoft Azure (Data Lake, Data Factory), Databricks, Apache Spark (PySpark), Python, SQL, Git, Confluence, Power BI, Power Apps, Dataverse, MS Sharepoint, Mural
Mansharan K.
Last position:
MS Fabric | Power BI Consultant at Gropyus Technologies GmbH
- Architected an enterprise Fabric-based data-product governance framework, authoring data contracts and schema-validation KPIs to enforce completeness, freshness and SLA standards across reporting data products.
- Operationalized pipeline observability by integrating Dagster orchestration data into automated data-health reporting, proactively validating data integrity and pipeline reliability before production reporting layers refresh.
- Established BI-as-Code and end-to-end governance through Git-based version control, CI/CD and Alpha→GA release management, strengthening lineage, schema-drift detection and production change traceability.
Hachem S.
Last position:
Machine Learning & BI Lecturer at EU Business School
- Taught ML, BI, and Big Data to Master's students.
Tech: Python, Power BI, SQL, ML, SnowflakeDB, databricks, MS Fabric, MongoDB, LLM
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
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.
Max D.
Last position:
Senior Software Engineer at Ynfinite
Senior Software Engineer with a core role in the architecture and product of Ynfinite, a European Webflow competitor (website builder and CMS).
- Architected a flow-based CMS configuration system with React Flow, custom graph state and node/edge interactions.
- Built a flexible visual Freeform Editor with recursive drag-and-drop, rich text editing and data bindings.
- Developed extensible inspector and reference systems for page links, components and assets.
- Built advanced CMS capabilities including interactive maps, content-type editing and CRM-connected forms.
- Technologies: TypeScript, React, Nest.js, Express.js, Prisma, PostgreSQL, Redis, BullMQ, Tailwind, Radix UI, TanStack Router
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.
Nitin B.
Last position:
Financial Analytics Lead at Independent Consultant
Led FP&A tech transformation for a 9-figure business – from resolving legacy technical debt to leading AI-native EPM implementation
- Driving end-to-end FP&A transformation, from architecture redesign through EPM tool selection to rollout
- Ran evaluation of 12+ EPM platforms, from vendor negotiation to selection framework tied to long-term planning
- Diagnosed constraints in financial planning architecture, presented findings to the CFO, and secured executive mandate to redesign FP&A infrastructure from the ground up
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
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.
Aleksei R.
Last position:
QA Engineer at Cybus GmbH
- Led QA and test automation for a distributed industrial IoT and data-integration platform connecting shop-floor systems with enterprise applications.
- Designed and maintained automated API and UI tests using Playwright, TypeScript, and Python, covering REST APIs, authentication, resource management, integrations, permissions, and end-to-end workflows.
- Tested distributed services, data pipelines, messaging infrastructure, containerized applications, and integrations across Kubernetes, OpenShift, Docker, and Docker Compose environments.
- Performed functional, integration, regression, endurance, resilience, load, chaos, and exploratory testing across complex distributed systems.
- Used Chaos Mesh and Toxiproxy for network fault injection, latency, packet loss, and partition scenarios to validate system resilience and failure handling.
- Automated infrastructure provisioning, configuration, and environment setup with Ansible and managed deployments using Kubernetes and Helm.
- Tested integrations with industrial protocols, messaging systems, databases, HTTP/REST APIs, and external enterprise systems using simulators, emulators, and dedicated test environments.
- Worked with MQTT, Kafka, SQL databases, InfluxDB, REST APIs, OAuth 2.0, JWT, LDAP, Active Directory, OpenLDAP, mTLS, TLS, MFA, SSO, and RBAC.
- Investigated and resolved 40+ defects across backend services, integrations, distributed components, and web applications, using AI-assisted analysis with Claude to accelerate debugging, test design, and root-cause investigation.
- Improved deployment reliability through Kubernetes/OpenShift configuration and health-check tuning and monitored distributed systems using Grafana, Prometheus, and Loki.
- Collaborated with developers and product teams on sprint planning, technical refinement, quality strategy, release testing, and continuous improvement.
- Applied AI-assisted and agentic testing approaches to expand automated coverage and identify complex edge cases.
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
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, Automotive
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
99%
Master's degree or higher
72%
Doctorate
12%
Certifications per freelancer
3
Most common languages
English, German, French
Speak two or more languages
98%
Based on our profile pool as of 5 Nov 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 5 Nov 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 (85%)
- Banking and Finance (38%)
- Automotive (36%)
- Education (36%)
- Professional Services (35%)
- Retail (31%)
- Manufacturing (29%)
- Healthcare (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 88 €, which corresponds to a daily rate of about 700 € based on an 8-hour working day.
Of the freelancers who have used Data Pipeline in their recent projects, 99% hold at least a Bachelor's degree, 72% hold at least a Master's degree, and 12% 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 (15%).
The most common industries among freelancers who have used Data Pipeline in their recent projects are Information Technology (85%), Banking and Finance (38%), and Automotive (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 (74%).
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
