Panagiotis Tsafaridis

Onboarded and conducted comprehensive documentation and system analysis to assess the existing data infrastructure, facilitating rapid integration and collaboration across functional data teams (modelling, processing, reporting). Collaboratively defined the architecture and project structure for a central data pipeline repository, including hierarchical standards, knowledge management strategies, and role-specific responsibilities, enhancing maintainability and onboarding speed. Evaluated and validated open-source data routing tools (Airbyte, Apache NiFi, Dragster) for ingest and sync requirements in retail analytics, including local benchmarking and error-state testing. Led the design and deployment of Airbyte in Kubernetes, creating customized Helm charts, securing secrets handling, and configuring Ingress with TLS and internal DNS routing, ensuring full API and UI accessibility. Troubleshot and resolved Ingress controller issues, iterating through multiple stages of debugging and testing, and documented setup and replication steps for scalable reuse. Mapped data models to ARTS standard, supporting schema alignment for ERP and reporting use cases, and coordinated review loops to align future data processing logic. Drafted strategic 1-pagers comparing MinIO, Pub/Sub, and routing architectures, providing technical guidance for architectural decisions and investment planning. Enabled secure access and authentication mechanisms, including initial evaluation for SAML integration, cluster-level configuration reviews, and service annotation improvements.

Designed and led the architecture of a centralized Infrastructure-as-Code (IaC) catalog using Terraform and Terragrunt to enable scalable, secure, and standardized observability infrastructure for over 40 engineering teams, significantly accelerating onboarding and reducing drift. Developed reusable modules for logging pipelines, index policies, monitors, dashboards, API keys, SAML authentication mappings, team roles, and service accounts, ensuring traceability and compliance across multiple business units and cloud environments. Built and rolled out a secure log archiving system using Datadog, Vector, and Azure Blob Storage, integrating dynamic IP whitelisting and lifecycle policies to meet regulatory and performance requirements. Introduced a governance framework aligned with internal standards (Monokel), embedding tagging conventions, naming strategies, and policy inheritance through Terragrunt, and enabling consistent resource hygiene at scale. Automated CI/CD workflows using GitHub and GitLab Actions with advanced matrix strategies, seed jobs, and dynamic JSON/HCL transformations, providing resilience and clarity in multi-environment, multi-team deployments. Championed product-oriented thinking by running enablement workshops, pairing sessions, and offsite planning initiatives to empower teams to treat their monitoring setups as internal products with ownership and accountability. Standardized team onboarding practices, including the creation of a scalable onboarding pipeline, practical training on Datadog, IaC, and logging architecture, and ongoing architectural mentoring to internal SRE and DevOps leads. Collaborated cross-functionally to align observability practices with broader IT and compliance strategies, continuously refining the shared IaC foundation based on team feedback and operational metrics.

Analyzed the existing infrastructure and optimized Terraform automation by migrating infrastructure repositories to Terraspace, ensuring streamlined deployments and improved modularity. Developed a dynamic GitHub Actions workflow for Terraspace, enabling fully automated deployments across multiple environments, reducing manual intervention and improving CI/CD efficiency. Enhanced monitoring capabilities by implementing Datadog logging pipelines with Grok processors, improving log categorization, and reducing noise by filtering unnecessary debug logs. Led the migration of AWS CloudWatch alerts for microservices, ensuring that monitoring configurations were standardized across testing, staging, and production environments. Optimized Datadog cost structure, refining log exclusion filters and implementing log-based monitoring strategies, reducing unnecessary logs and improving alert precision. Debugged and resolved critical pipeline issues, ensuring smooth application rollouts and troubleshooting AWS Lambda service configurations for scalability and performance improvements. Provided expert guidance on best practices for AWS infrastructure, Datadog monitoring, and Terraform-based automation, empowering internal teams through hands-on workshops and consulting sessions.