Lead Platform Engineer · Data Platform Architect · Distributed Systems

I design and build engineering and data platforms for applications, analytics, and AI workloads.

My focus is on turning complex infrastructure into reliable, secure, and maintainable platform products that engineering teams can operate and evolve without constant manual intervention.

• Internal developer platforms
• Self-service infrastructure
• Infrastructure as Code
• CI/CD and GitOps
• Kubernetes-based platforms
• Reproducible environments and operations

• Event-driven and streaming architectures
• Data ingestion and processing
• Distributed query engines
• Lakehouse and S3-compatible storage
• Analytics and AI infrastructure
• Data access and governance

• Identity and access management
• OAuth2, OIDC, and enterprise directory integration
• Role-based access control
• Policy as Code
• Fine-grained authorization for data platforms
• Secrets and service identity management

• Observability and production readiness
• Metrics, logs, traces, and alerting
• Failure analysis and incident response
• Operational automation
• Capacity, scalability, and resilience
• Safe and controlled change management

Data and distributed systems

Kafka · Debezium · Trino · Spark · Airflow · S3 · MinIO · Lakehouse

Platforms and infrastructure

Kubernetes · Docker · Terraform · Ansible · Helm · GitLab CI/CD · GitOps

Identity and security

Keycloak · Active Directory · OAuth2/OIDC · RBAC · OPA · Policy as Code

Observability

Prometheus · Grafana · Loki · Mimir · OpenTelemetry

Software engineering

Python · Go · Java · SQL · REST APIs · Distributed Systems Architecture

I combine architectural responsibility with hands-on engineering.

I validate important decisions through implementation, prototypes, integration, and production experience. I consider architecture a continuous process of making and testing technical decisions rather than a separate documentation phase.

I prefer solutions that:

• make system state and ownership explicit;
• reduce hidden assumptions;
• remain understandable to other engineers;
• can be deployed and operated reproducibly;
• support safe and controlled changes;
• minimize dependency on individual specialists;
• remain maintainable over the long term.

• Platform APIs and control planes
• Internal developer platforms
• Data and AI infrastructure
• Policy as Code
• Fine-grained authorization
• Distributed systems reliability
• AI-assisted software engineering
• Open-source platform products

Public repositories, reference implementations, architectural experiments, and technical notes will be listed here as they become ready for publication.

• Website: letenkov.ru
• Email: [email protected]
• Telegram: @letenkov
• LinkedIn: linkedin.com/in/letenkov