E-mobility software engineer for embedded systems. Now I’m transitioning to Data Engineering and ML systems out of strong interest and curiosity.

Experienced Embedded Software Engineer with a strong foundation in automation, data-driven systems, and scalable software architectures.
Currently transitioning into Data Engineering and Applied Machine Learning, leveraging a deep understanding of system design, data flows, and distributed computation.

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  Engineered complex automation and control systems using PA-Base/Script, an object-oriented scripting environment conceptually similar to Python/C++ which helped me build strong foundations in modular software design, data manipulation, and process automation.

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  1. Designed and deployed automated data acquisition and transformation pipelines for large-scale battery testing which are analogous to modern ETL (Extract, Transform, Load) workflows in data engineering.
  2. Implemented process control flows via DAG-based orchestration (PA-Graph), mirroring dependency management in tools like Apache Airflow.

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  1. Developed structured and distributed databases for managing cell, pack, and end-of-line test data which conceptually aligned with PostgreSQL, AWS RDS, and DynamoDB architectures.
  2. Implemented cloud-based data synchronization for global test environments, paralleling AWS S3 and Azure Data Lake solutions.

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  1. Analyzed large-scale battery performance data to detect trends and anomalies using statistical and algorithmic reasoning and laying groundwork for machine learning workflows.
  2. Built user-facing dashboards (PA-Design) for visualization and reporting, comparable to frameworks like Streamlit or Plotly.

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  1. Built real-time monitoring solutions for distributed test systems, providing insight into data quality, system health, and performance which conceptually aligned with Prometheus, Grafana, and AWS CloudWatch.
  2. Defined alerting and metric-tracking logic for anomaly detection and proactive maintenance.

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  Automated deployment and testing pipelines for hardware-software integration which extends continuous integration and delivery (CI/CD) concepts into data and MLOps workflows.

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  Led global customer training sessions across Europe, the USA, and China, authored internal documentation and user guides to standardize testing and data workflows.

• Developed full-stack applications and Data science / ML-based projects, demonstrating proficiency across both software engineering and data infrastructure layers.
• Familiar with AWS Cloud, Python, SQL, Databricks, Terraform, Docker, and CI/CD pipelines.

My experience in embedded systems taught me to build reliable, data-centric automation in distributed environments :— skills that map directly to modern data engineering and cloud computing.

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  Transitioning to working with production-grade data engineering, data science, and applied ML projects.

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  AWS Cloud Solutions: Glue, Lambda, API Gateway, S3, IaC (Terraform, CloudFormation), Simple Data Lake, CloudWatch, Cost Explorer, RDS, DynamoDB, IAM, VPC Security, Databricks, Jenkins (CI/CD), Airflow (DAGs).

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  AWS (Cloud): Lambda, S3, API Gateway, RDS, DynamoDB, IAM, Service Catalog, Terraform (IaC), CloudWatch, Cost Explorer, EKS, SQS, Glue, Athena, VPC, and others.

  Programming & Tools: Python, SQL, Unix Shell Scripting, PySpark, ETL.

  DevOps & Automation: CI/CD, Git, Jenkins, Airflow, Terraform (IaC), Kafka (Basic), Containerisation (EKS, Docker).

  Design & Architecture: System Design, Client-Server Architecture, Microservices, Serverless Architecture, Event-Driven Architecture, Data Modeling, Database Design.

  Observability & Monitoring: OpenTelemetry (Otel), Jaeger, Databricks, Prometheus, Grafana, custom DIY Monitoring & Observability Panel.

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  1. Knowledge app integrates multiple APIs + Supabase(PostgreSQL) + hosting environment + recommendation system (repo is private, permission-based access).
  2. Medium Article that explains the detailed workflow of the Knowledge-app.
  3. Personal blogging website built from scratch — roadmap includes adding a text-to-speech model (private repo).
  4. Medium Article explaining the workings of Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) in depth.

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  1. Agentic Knowledge graphs construction
  2. Building AI Agents and Agentic Workflows

Moving closer to downstream data roles through projects, certifications, and writing:

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  • Data Engineering — DeepLearning.AI (4-course specialization using AWS)
  • Data Science — WBS Academy, Berlin
  • Deep Learning — DeepLearning.AI (5-course specialization)
  • RAG — DeepLearning.AI
  • Docker & Kubernetes
  • Short courses: GCP Essential Training; Statistics (3-part series)

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  • The Knowledge Drip
  • The Complete Guide to RAG: Part I — Operational Mechanics
  • The Complete Guide to RAG: Part II — Application Mechanics
  • Understanding Deep Neural Networks — Foundations & Intuition (1a)
  • Neural Network Mechanics (1b)
  • Specifics of Deep Neural Nets & Bottlenecks (1c)
  • Demystifying Word Embeddings: Neural Nets → Contrastive Learning

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  Open to and excited about collaborating on end-to-end data engineering, data science, and applied ML projects — from small builds to production-grade pipelines.

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  From embedded systems to end-to-end data workflows: engineering pipelines, applied ML, RAG and deep learning — deployed with DataOps/DevOps practices (CI/CD, IaC, automation, monitoring, Docker/Kubernetes).

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