
Big Data Developers in Germany
in minutes with the power of AIScale distributed pipelines with Spark, Kafka, and modern cloud lakehouses. Access thoroughly vetted, available big data engineers ready to accelerate your data initiatives.
Meet FRATCH Big Data Developers in Germany
Ravindra K.
Last position:
Data Engineer (Team Lead) at OPTUM – UnitedHealth Group
- Lakehouse build: Designed and built the member & eligibility domain of an Azure Databricks healthcare lakehouse, ingesting SQL Server, file and REST/SFTP sources (JSON, CSV, XML, Parquet) via Azure Data Factory into ADLS Gen2.
- Medallion pipelines: Transformed data through bronze, silver and gold Delta Lake layers with PySpark and Spark SQL; used Delta Live Tables / Lakeflow with built-in expectations for declarative, quality-checked pipelines.
- Platform modernisation: Delivered a SQL Server-to-cloud platform: ADF → ADLS Gen2 → Databricks → dbt Core models → Snowflake → Power BI, replacing legacy reporting with governed analytics.
- Robust ETL: Built metadata-driven, idempotent full, incremental and watermark-driven loads with schema evolution and failure-state handling, improving reliability and re-runnability.
- Data modelling: Designed Kimball star schemas with SCD Type 1 & 2 dimensions; tuned slow queries and pipelines using execution plans.
- Governance & security: Applied Unity Catalog RBAC, lineage and audit logging, with Azure Key Vault secrets and managed identities, to meet HIPAA and GDPR requirements.
- Leadership & mentoring: Led a 6-person team (2 data engineers, 2 analysts, BI developer, QA); mentored engineers, set delivery standards and coordinated with international stakeholders.
- Delivery & AI-assisted work: Ran CI/CD and work tracking in Azure DevOps; built Power BI semantic models with DAX and RLS; used Genie Code, Claude and Copilot to accelerate PySpark/SQL development, DAX and documentation.
Jorge M.
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
Serhat K.
Last position:
Kafka issues in production at Fiverr client
Fiverr client
- Fixed consumer issues in a production Kafka environment.
- Analyzed consumer lag with Datadog.
- Added synthetic tests to detect issues early.
Stack: Kafka, Datadog, Consumer Groups
Karthikeyan R.
Last position:
Full-Stack Developer — Own Product at Self-employed
Java 21 · Spring Boot 3 · Keycloak · PostgreSQL · Docker · Nginx · GitHub Actions · DigitalOcean · React 18 · TypeScript · Plasmo
- Architected and shipped a production-ready Job Application Tracker end-to-end: REST API with 5-stage workflow, pagination, sorting, and dynamic filtering — full ownership from design to live cloud deployment on DigitalOcean.
- Implemented production-grade identity management: OAuth 2.0 / OpenID Connect / JWT / RBAC via Keycloak, applying Hexagonal Architecture and DDD principles.
- Built automated CI/CD pipeline (GitHub Actions); containerised with Docker; Nginx reverse proxy with path-based routing and SSL termination.
- Developed a Chrome Extension (Plasmo framework, Manifest V3) that auto-fills job applications directly from LinkedIn into the tracker — demonstrates full product thinking across backend API and browser client.
Anton K.
Last position:
Head of Overall Technical Integration NSC / Hadoop Cloud Development at IABG
Head of overall technical integration NSC (National Secure Cloud, project with approx. 60 employees).
Technical integration of all subprojects into one product, definition of interfaces and basic components of a cloud including hardware, technical architecture of the IABG platform.
Development of a Cloud Management Platform (CMP) capable of creating private/mixed clouds of any complexity based on a textual description with one click or interactively.
CMP also includes the complete hardware management lifecycle.
Kubernetes, OpenStack and Hadoop are used as the foundation.
The management layer includes Harbor, Gitea, Longhorn, Keycloak, Rancher and Jenkins, which are configured automatically.
Private cloud can run any customer workloads, including a full Hadoop layer with HDFS, Spark, MapReduce, Mesos, HBase and around 20 additional ML/DL technologies.
Hadoop worker clusters can also be installed automatically without Kubernetes on bare metal or commodity hardware.
OpenStack with Nova, Neutron, Ironic, Swift, Cinder, Ceph.
Development of a Java application Rudi: SOAP, REST, containers, DB.
Technologies: Kubernetes (K3s, Rke2, Minikube, Harbor, Gitea, Jenkins, Longhorn, Keycloak, Rancher), OpenStack (Nova, Neutron, Keystone, Swift, Ceph, Cinder, Sahara, Magnum, Kayobe, Kolla, Bigrost, Ironic), Hadoop (HDFS, Ambari, Solr, Livy, Ranger, YARN, Tez, HBase, Kafka, Hive, Zookeeper, MapReduce, Spark, Oozie, Flink), virtualization (Kubernetes (K3S), VMware, Oracle), scripting (Ansible, Puppet, Juju, Shell, Groovy, Gradle, Maven).
Jörg-Ulrich H.
Last position:
Data flows for health insurance providers
- Further development and creation of data flows for health insurance providers
- Data management across various storage systems (DB2, MSSQL, PostgreSQL, S3, custom APIs, ...)
- Documentation and training
- Planning and deployment of NiFi 2.x (major upgrade)
- Integrating Grafana for visualization, monitoring, and alerting
- Extensive use of the NiFi API to continuously monitor the system and its components
Discover over 15,000 top freelancers
Big Data Developers statistics
Aggregated from the professional profiles of matched freelancers.
Experience
17 years

Position duration
2.2 years

Positions per freelancer
17

Top business areas
Information Technology, Product Development, Business Intelligence

Top industries
Information Technology, Automotive, Banking and Finance

Certification focus areas
Information Technology, Business Intelligence, Operations
Bachelor's degree or higher
100%
Master's degree or higher
40%

Certifications per freelancer
4

Most common languages
German, English, Spanish

Speak two or more languages
100%
Based on our profile pool as of 2 Nov 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this role in Germany 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 for Big Data Developers in Germany
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 2 Nov 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Big Data Developers 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 (100%)
- Automotive (67%)
- Banking and Finance (67%)
- Insurance (67%)
- Energy (50%)
- Manufacturing (50%)
- Professional Services (50%)
- Tourism (50%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the role
Engineering Large-Scale Data Platforms
A big data developer designs, builds, and maintains systems that ingest, process, and store massive data volumes. These specialists transform raw, unstructured telemetry, transaction logs, and event streams into structured assets that analytics and machine learning applications can query reliably.
Core Engineering Deliverables
Freelance data platform engineers solve complex throughput and latency challenges across the data lifecycle:
- Resilient batch and real-time ingestion pipelines using Apache Spark, Flink, and Kafka
- Scalable data lakehouse architectures built on Delta Lake, Apache Iceberg, or Snowflake
- Workflow orchestration scripts using Apache Airflow, Prefect, or Dagster
- Optimized storage partitions and compute resource management in AWS, GCP, or Azure
Essential Tools and Technical Breadth
Strong developers demonstrate deep expertise in Scala, Python, and Java, alongside advanced distributed SQL. They master containerization with Docker and Kubernetes for pipeline deployment. Experience with NoSQL data stores like Cassandra or MongoDB ensures data is readily accessible for low-latency operational use cases.
Driving Data Projects in German Industries
Enterprises across Germany rely on freelance big data programmers during major transformations, including migrations from legacy on-premise clusters to modern cloud setups. Automotive, industrial manufacturing, and fintech organizations regularly hire external specialists to process connected-vehicle telemetry, factory IoT metrics, and fraud-detection streams under strict internal governance.
When to Bring in an External Specialist
Companies engage freelance data engineers when in-house teams lack deep distributed systems experience or face tight delivery deadlines for data migrations. Bringing in an external expert accelerates the foundation of reliable data pipelines without long hiring cycles, allowing internal engineering teams to focus on core product features.
Hallmarks of Top-Tier Talent
Exceptional big data specialists prioritize cluster cost efficiency, data quality testing, and idempotency over simple script execution. They write clean, automated code with integrated CI/CD pipelines, document their data models clearly, and understand how to maintain strict compliance with enterprise data privacy standards.
Frequently asked questions
Before you brief your next project: the most common questions about Big Data Developers.
A big data developer focuses on building, maintaining, and scaling data processing pipelines capable of handling terabytes to petabytes of information. They implement batch and streaming architectures using tools like Apache Spark and Kafka. Their deliverable is a stable, clean data supply for downstream machine learning and analytics workflows.
While often used interchangeably with a big data engineer, this role leans heavily toward distributed compute engines, storage performance, and high-throughput pipelines. A data engineer may handle broader relational ETL tasks, whereas a data scientist builds statistical models on the prepared datasets. The developer provides the scalable underlying infrastructure and pipeline code.
A seasoned freelance big data engineer typically works in Python, Scala, or Java, combined with advanced SQL. In terms of frameworks, production experience with distributed processing engines like Spark or Flink and streaming platforms like Kafka or Pulsar is vital. Knowledge of cloud ecosystems like AWS, Azure, or Google Cloud Platform is also required.
Companies engage a freelance big data developer to bridge short-term technical knowledge gaps during complex migrations, platform refactoring, or initial architecture setups. Freelancers provide immediate production expertise without lengthy recruitment cycles. Once the core data pipelines are established and documented, internal teams can maintain them.
Most technical environments across Germany accept English as the primary project language for an external big data programmer, especially within international engineering teams. However, German language capabilities are often a strong plus when integrating with traditional enterprise IT environments or cross-departmental business units.
Yes, nearly all pipeline design, cluster tuning, and code development by a big data consultant can be handled remotely using secure cloud access. Many organizations in Germany favor a hybrid schedule, requesting on-site attendance primarily during project kickoffs, architecture alignment sessions, or critical deployment windows.
Evaluate a big data specialist by reviewing their experience with large-scale production outages, partition optimization, and compute cost reductions. High-caliber experts discuss not just how to run pipelines, but how they ensure idempotency, data lineage, automated testing, and comprehensive monitoring across the data lifecycle.
A professional big data engineer in Germany must be well-versed in data sovereignty regulations and enterprise privacy practices, notably GDPR. They ensure pipelines anonymize sensitive fields, implement proper encryption in transit and at rest, and adhere to strict access controls within sovereign cloud regions.
The average hourly rate for Big Data Developers in Germany is 102 €, which corresponds to a daily rate of about 813 € based on an 8-hour working day.
Of the freelancers working as Big Data Developers in Germany, 100% hold at least a Bachelor's degree and 40% hold at least a Master's degree.
On average, freelancers working as Big Data Developers in Germany have 17 years of professional experience, with a single engagement typically lasting around 2.2 years.
The most common languages among freelancers working as Big Data Developers in Germany are German (100%), English (83%), and Spanish (17%).
The most common industries among freelancers working as Big Data Developers in Germany are Information Technology (100%), Automotive (67%), and Banking and Finance (67%).
The most common business areas among freelancers working as Big Data Developers in Germany are Information Technology (100%), Product Development (100%), and Business Intelligence (83%).
FRATCH Big Data Developers main locations
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.
Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
Would you rather directly get in touch?
We always have the time for a call or email!
