Building reliable data & ML platforms.

Freelance Data & Cloud Engineer · MLOps & ML Platforms

I design and build cloud-native ML & data platforms, automated ML pipelines, and production-grade infrastructure that reduce manual work, improve reliability, and accelerate delivery for data & engineering teams.

01. About Me

From scattered data & models to reliable production-ready ML platforms

I’m a Germany-based Data & Cloud Engineer focused on building cloud-native ML & data platforms, automated model pipelines, and production-ready infrastructure. I help teams move from scattered data and prototype notebooks to robust, observable systems — covering everything from data ingestion and transformation to model deployment, monitoring, and lifecycle management.

Experience

  • 8+ years in IT & Software Engineering
  • 5+ years in data & ML solutions
  • 2+ years in data platforms & MLOps-focused solutions

Selected Certs

  • Azure Data Scientist
  • Databricks Data Engineer
  • Snowflake Core
  • SAS ML

Focus

  • ETL & ELT Automation
  • Data Platform Engineering
  • Cloud-native Development (Kubernetes, containers)
  • Monitoring & Observability
  • Automation of data & ML pipelines

Location

  • Germany (Fluent in German)
  • Remote (Worldwide. Fluent in English)

02. Services

Specialized Core Capabilities

ML & Data Platform Engineering

Design and build cloud-native ML & data platforms on Kubernetes and major clouds. From ingestion and storage to serving and access layers, with security, reliability, and scalability by design.

MLOps & Model Lifecycle Automation

End-to-end automation for training, validation, packaging, and deployment of ML models. CI/CD for ML, model registries, feature pipelines, and reproducible workflows from experiment to production.

Monitoring, Governance & Observability

Enterprise-grade observability for data & ML workloads: logging, metrics, dashboards, SLOs, data & model quality checks, audit-ready tracking of runs, models, and data lineage.

Cloud Infrastructure & CI/CD for ML Workloads

Infrastructure-as-Code and CI/CD pipelines for ML and data workloads. Kubernetes clusters, environments, and deployment workflows that keep platforms stable while enabling fast delivery.

03. Case Studies

Featured Platform & MLOps Results

Selected projects showcasing ML & data platform engineering, MLOps, and automation.

Churn-Pipeline

Customer-Churn (Showcase)

Freelance | Role: Cloud & MLOps Engineer

Self-initiated showcase project (no client data). Built a reproducible pipeline demonstrating churn prediction with MLflow tracking, testing, and containerized deployment. Includes a reproducible MLOps-style pipeline with tracked experiments and automated deployment.

Outcome: Reproducible training pipeline with CI/CD, MLflow tracking, and automated artifact management.

Python FastAPI MLflow Docker Kubernetes GitHub Actions Airflow PostgreSQL Prometheus Grafana ArgoCD
Data Model Migration

Data Model Migration and Enhancement

Insurance | Role: Data Engineer

Migrated existing data model and ETL workflows from AWS Glue to PostgreSQL. Extended the data model to support new reporting needs and created Tableau dashboards for clearer and faster data insights.

Outcome: Reduced infrastructure costs and improved data processing speed. Resolved recurring data integrity issues.

AWS Glue PostgreSQL SQL Tableau Talend ETL Pipelines Data Warehousing
Viya Model Pipeline

Data Ingestion Refactor

CBTW | Role: Data & ML Engineer

Rebuilt ingestion pipelines with validation and error recovery. Reduced latency and improved reliability across all data jobs. Prepared the platform for ML automation and production-ready pipelines.

Python SAS SAS Viya ETL Pipelines Data Validation Data Quality
Projectmatch

Automated Candidate Scoring Pipeline (Showcase)

Freelance | Role: Data & Cloud Engineer

Self-initiated showcase project (no client data). Built a reproducible pipeline demonstrating automated data collection and analysis of freelance project postings with an LLM scoring how well a profile matches each listing, plus API integration. Includes a reproducible MLOps-style pipeline with tracked experiments and automated deployment.

Outcome: Fully automated workflow from data collection to AI-based matching with CI/CD and observability, including an integrated web app and API.

Python MLflow FastAPI Docker GitHub Actions PostgreSQL

SAS Viya Platform on Kubernetes

CBTW | Role: Data & Platform Engineer (Viya Platform Team)

Provisioned, operated, and extended a containerized SAS Viya environment on Kubernetes as a SaaS offering; responsible for stability, maintenance, troubleshooting, and platform evolution. Provisioned and operated a Kubernetes-based analytics platform designed for MLOps workloads with monitoring, scaling, and incident response.

Outcome: Reliable, scalable Viya platform with documented runbooks and fast incident resolution.

SAS Viya Kubernetes Docker Helm Prometheus Grafana GitLab CI/CD
SAS platform operations

SAS 9.4 Platform Operations

Insurance | Role: Data & Platform Engineer

Ran a production SAS 9.4 platform for risk and reporting workloads, including hotfix/security patch rollouts and incident analysis with business teams. Includes observability, release management, and automated workflow operations for a regulated analytics platform.

Outcome: Stable, audit-ready platform with fewer production incidents.

SAS 9.4 Base SAS SAS Macros Linux Monitoring Platform Operations
Automated account statement generation

Automated Account Statement Generation

Bank | Role: SAS Developer

Digitized a manual account-statement process in SAS 9.4: co-designed with business/architecture and built an STP app with input UI, business logic, and SAS code.

Outcome: Cycle time reduced from weeks to days and ready for pilot and production rollout.

SAS 9.4 SAS Stored Processes (STP) Base SAS SAS Macros SQL Web UI
SAS Viya Migration

Migration SAS 9.4 -> SAS Viya

Insurance | Role: Migration Engineer / SAS Modernization

Led migration from SAS 9.4 to SAS Viya and modernized existing workloads for the target platform. Prepared the platform for ML automation and production-ready pipelines.

Outcome: Viya target architecture with migrated core processes and clear cutover steps.

SAS 9.4 SAS Viya Docker Kubernetes Platform Modernization Migration
Cloud Detection Model

Cloud Detection (Competition)

Freelance | Role: Data & ML Engineer

Build a deep learning model to detect clouds in satellite images using Encoder-Decoder architecture with resnet, resnext & efficientnet backbones.

Outcome: Achieved top 3% ranking on DrivenData.

Python TensorFlow Computer Vision Satellite Imagery Remote Sensing Keras

04. Contact

Let’s Build Your ML Platform

Need help building a reliable ML & data platform, automating ML pipelines, or operating cloud-native analytics systems? Tell me about your stack and challenges, and we’ll see how I can help.