The client needed a modern, secure, and fully automated Terraform deployment pipeline. Their existing workflow lacked security scanning, governance, and auditability.
Designed and implemented state?of?the?art Terraform CI/CD pipeline with automated security scanning (static analysis, policy-as-code, secrets detection).
Built POC pipeline architecture enabling safe multi?environment deployments.
Conducted security audit, identifying critical vulnerabilities across networking, IAM, and secrets management.
Created with stakeholders, including network segmentation, WAF introduction, and improved secrets lifecycle.
Reduced infrastructure deployment risk significantly.
Enabled the client to adopt secure-by-default cloud practices.
Provided a roadmap that improved their overall cloud security posture.
Project Overview
The client required a scalable platform capable of ingesting, processing, and analyzing large volumes of high?frequency time series data. Their existing tooling was fragmented, slow, and unable to support real?time analytics or machine learning workloads.My Contribution
I initiated and led the project from the initial concept phase. This included defining the vision, presenting the business case to stakeholders, securing budget, and assembling a small engineering team to deliver the MVP.
Key Achievements
Designed the full architecture for a cloud?native time series analytics platform supporting both real?time and batch processing.
Implemented a streaming data pipeline using Kafka to reliably ingest and distribute time series data from multiple sources.
Built distributed processing components using PySpark and Python to compute metrics and prepare data for downstream analytics.
Developed a real?time anomaly detection system using Numpy and RiverML, deployed on ECS containers for scalable inference.
Established operational foundations including monitoring, alerting, CI/CD, and infrastructure automation.
Coordinated a team of three engineers and collaborated with data scientists and domain experts to refine requirements and validate outputs.
Impact
Delivered an MVP enabling real?time insights into system performance and operational anomalies.
Provided a scalable foundation for future predictive analytics and machine learning initiatives.
Reduced manual analysis effort and improved the speed and accuracy of decision?making across teams.
The client operated 15+ AWS accounts with >$1M annual spend but lacked centralized governance, security controls, and consistent deployment practices.
As part of the engineering team behind Bundesliga Match Facts, I contributed to building one of the most advanced real?time sports analytics platforms in professional football. The system ingests live match data, processes it through multiple analytics services, and delivers insights to broadcasters, clubs, and fans within seconds.
I served as a core backend engineer responsible for designing, implementing, and maintaining the data backbone that powers the analytics pipeline.
Led development of a new Kafka?based backend enabling high?throughput, low?latency communication between analytics services and data ingestion layers.
Designed and implemented two new Match Facts (Skill and Set Piece Threat), from concept to production deployment.
Refactored major parts of the platform, improving reliability, scalability, and maintainability.
Built event?driven microservices using Python, deployed on AWS ECS, with DynamoDB and Lambda for supporting workloads.
Ensured the system met strict performance requirements for live broadcast environments.
Collaborated closely with data scientists, frontend teams, and AWS architects to deliver new features on tight timelines.
Enabled real?time delivery of advanced football analytics to millions of viewers.
Improved system stability and throughput during peak match?day load.
Contributed to features highlighted in official AWS publications.
Certifications:
Infrastructure as Code:
Technologies:
Side Projects
The client needed a modern, secure, and fully automated Terraform deployment pipeline. Their existing workflow lacked security scanning, governance, and auditability.
Designed and implemented state?of?the?art Terraform CI/CD pipeline with automated security scanning (static analysis, policy-as-code, secrets detection).
Built POC pipeline architecture enabling safe multi?environment deployments.
Conducted security audit, identifying critical vulnerabilities across networking, IAM, and secrets management.
Created with stakeholders, including network segmentation, WAF introduction, and improved secrets lifecycle.
Reduced infrastructure deployment risk significantly.
Enabled the client to adopt secure-by-default cloud practices.
Provided a roadmap that improved their overall cloud security posture.
Project Overview
The client required a scalable platform capable of ingesting, processing, and analyzing large volumes of high?frequency time series data. Their existing tooling was fragmented, slow, and unable to support real?time analytics or machine learning workloads.My Contribution
I initiated and led the project from the initial concept phase. This included defining the vision, presenting the business case to stakeholders, securing budget, and assembling a small engineering team to deliver the MVP.
Key Achievements
Designed the full architecture for a cloud?native time series analytics platform supporting both real?time and batch processing.
Implemented a streaming data pipeline using Kafka to reliably ingest and distribute time series data from multiple sources.
Built distributed processing components using PySpark and Python to compute metrics and prepare data for downstream analytics.
Developed a real?time anomaly detection system using Numpy and RiverML, deployed on ECS containers for scalable inference.
Established operational foundations including monitoring, alerting, CI/CD, and infrastructure automation.
Coordinated a team of three engineers and collaborated with data scientists and domain experts to refine requirements and validate outputs.
Impact
Delivered an MVP enabling real?time insights into system performance and operational anomalies.
Provided a scalable foundation for future predictive analytics and machine learning initiatives.
Reduced manual analysis effort and improved the speed and accuracy of decision?making across teams.
The client operated 15+ AWS accounts with >$1M annual spend but lacked centralized governance, security controls, and consistent deployment practices.
As part of the engineering team behind Bundesliga Match Facts, I contributed to building one of the most advanced real?time sports analytics platforms in professional football. The system ingests live match data, processes it through multiple analytics services, and delivers insights to broadcasters, clubs, and fans within seconds.
I served as a core backend engineer responsible for designing, implementing, and maintaining the data backbone that powers the analytics pipeline.
Led development of a new Kafka?based backend enabling high?throughput, low?latency communication between analytics services and data ingestion layers.
Designed and implemented two new Match Facts (Skill and Set Piece Threat), from concept to production deployment.
Refactored major parts of the platform, improving reliability, scalability, and maintainability.
Built event?driven microservices using Python, deployed on AWS ECS, with DynamoDB and Lambda for supporting workloads.
Ensured the system met strict performance requirements for live broadcast environments.
Collaborated closely with data scientists, frontend teams, and AWS architects to deliver new features on tight timelines.
Enabled real?time delivery of advanced football analytics to millions of viewers.
Improved system stability and throughput during peak match?day load.
Contributed to features highlighted in official AWS publications.
Certifications:
Infrastructure as Code:
Technologies:
Side Projects