Kiryl MiadzvedzeuDownload CV
Event-driven • AWS • Java

Systems that stay understandable as they grow

Kiryl Miadzvedzeu — Java / Cloud Software Engineer, Warsaw. Six years building event-driven platforms on AWS (Step Functions, Lambda, EventBridge, DynamoDB) and migrating legacy monoliths into microservices without breaking what already works.

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Kiryl Miadzvedzeu

About

For me the point of the job is solving real business problems, not just writing code — and good agile practice is how that actually happens. Refinements, retrospectives, and three-amigos sessions matter, because that's where misunderstandings get cleared up before they turn into rework.

Clear, transparent systems give more stable results than clever ones. I build things to be easy to reason about, and I've found that small, incremental improvements tend to add up to serious results over time. I lean on my team and trust it, while everyone keeps individual ownership of their own work — knowledge is best shared openly rather than locked in one person's head.

Risk is best weighed before a task is taken on, not after. As a backend engineer, "what happens if this goes wrong" is always running in the background — is there a plan B, a retry, a fallback, thought through from the start.

At a glance

  • • 6+ years, Java / AWS / event-driven systems
  • • Warsaw, Poland · B2B · Remote / Hybrid / On-site
  • • English B2 · Russian C2 · Belarusian C2 · Polish A2
  • • Currently: Cloud Infrastructure & Backend Engineer at Godel Technologies

Experience

  • Cloud Infrastructure Engineer / Backend Software Engineer — Godel Technologies

    Jul 2025 – Present

    • Shared Kubernetes platform on AWS for several product teams (insurance domain)
    • Focus areas: endpoint security, performance of the busiest services, observability
    • Driving the team's AI-first adoption roadmap
    → Full case study

  • Backend Software Engineer — Godel Technologies

    2024 – 2025

    • Event-driven workflows: Step Functions, Lambda, EventBridge, SQS
    • Team: 5 backend developers, 1 QA, 1 DevOps, 1 Tech Lead, 1 BA, 1 ADC · Scrum + Kanban
    • −25% Lambda cost, −30% latency via optimization
    → Full case study

  • Backend Software Engineer — Godel Technologies

    2022 – 2024

    • Smart metering platform; hundreds of microservices
    • Java 8 → 17, Spring 2.3 → 3.0 modernization
    • 98% bug-free releases · Scrum + Kanban hybrid
    → Full case study

  • Backend Software Engineer — SolbegSoft / Helmes Group

    2021 – 2022

    • Risk management & asset protection for automotive/property insurance
    • Team: 1 Tech Lead + 5 developers + 1 ADC · Scrum + Kanban hybrid
    • Migrated parts of a legacy monolith to microservices; QA hardening
    → Full case study

  • Backend Software Engineer — Automotive Group — Calendar Management System

    2020 – 2021

    • Greenfield internal Google-style calendar system
    • Sole backend engineer in a 6-person team (1 BE + 5 FE)
    • Built from scratch in ~10 months + 2 months production support
    → Full case study

Godel Platform — Security, Performance & Observability

Project. A Kubernetes platform on AWS, shared by several product teams in the insurance domain. My work splits roughly evenly between building backend services and keeping the platform reliable and secure. Over the past year the focus has been on three things the team feels directly: the security of our public endpoints, the performance of the busiest services, and how fast we can track down a bug in production.

Key decisions & practices

  • • Automated DAST on every release for public-facing endpoints — security checks used to be manual and easy to skip.
  • • Load & performance testing (Gatling) on the heaviest services, used to prioritize which ones move to more scalable approaches.
  • • Unified logging across the platform, replacing a mix of formats that made cross-service tracing slow.
  • • Legacy hardening: untangling tightly coupled older code so it's safer to change without breaking what already works.
  • • Secrets management: moved secrets out of static config into a proper secrets store.

Platform operations

  • • Kubernetes workloads across environments via Helm and FluxCD.
  • • Terraform for the AWS setup: IAM roles, OIDC, and supporting resources.
  • • GitLab CI pipelines for build, test, and deployment.
  • • Driving the team's AI-first adoption roadmap, including a focus group using Claude in production for analytics.

My role

  • • Day-to-day: build new microservices and improve deep legacy code.
  • • Set up DAST and performance-testing pipelines, and acted on their findings.
  • • Unified logging/observability approach across services.
  • • Kubernetes, Terraform, and CI/CD ownership alongside feature work.

Stack

Java 21, Groovy, Spring Boot, Kafka, RabbitMQ, Kubernetes, Helm, FluxCD, Terraform, Docker, GitLab CI, AWS (EKS, IAM, OIDC, IRSA, SQS, SNS, EventBridge, DynamoDB, Lambda), MySQL, Liquibase, Gatling, Spock, Testcontainers, Datadog, Micrometer, incident.io.

Control-Plane: Event-driven tenant workflows

Project. Multi-tenant control-plane to orchestrate tenant lifecycle (onboarding, changes, offboarding) with AWS Step Functions for orchestration, Lambda for compute, EventBridge for scheduling/triggers, and SQS (+DLQ) for decoupling. Strong idempotency (“exactly-once perception”), retries with jitter/backoff, compensations for saga steps, and end-to-end observability.

Key decisions & patterns

  • • Saga orchestration (retries with jitter/backoff, timeouts, circuit-breakers).
  • • Idempotency: request keys + DynamoDB conditional writes → exactly-once perception.
  • • Fan-out/fan-in via Parallel; async jobs in SQS (work/retry/DLQ) + outbox.
  • • Observability: structured logs, RED/USE, X-Ray, canaries; SLO/SLI dashboards.
  • • Testing: contract tests, local Step Functions, Testcontainers, fault injection.
  • • Hardening: exponential backoff + jitter, poison-pill quarantine, dedupe, DLQ alarms.

Impact

  • • Provisioned Concurrency cut cold starts to near zero during business hours.
  • • ~−30% p95 per orchestration step via batching, payload slimming, fewer network hops.
  • • SLO: p95 ≤ 300 ms, tracked via CloudWatch dashboards and alarms.

My role

  • • Designed state machines, compensations, and idempotency strategies.
  • • Implemented Lambda handlers, SQS consumers, and inter-service contracts.
  • • Performance/cost tuning (Provisioned Concurrency, batching, memory tuning).
  • • Dashboards/alerts and on-call runbooks; cutovers and post-mortems.

Stack

AWS Step Functions, Lambda (Java), EventBridge, SQS, DynamoDB, API Gateway, Terraform, CloudWatch/X-Ray, GitHub Actions; Testcontainers, JUnit/Mockito, contract tests.

Shell Energy — Smart Metering Platform

Real-time metering for 2,200,000+ users; multiple meters per user (gas + electricity). A change-frozen XML monolith was wrapped by adapters and gradually strangled to WebFlux microservices on Kubernetes. Observability via Prometheus/Grafana with RPS and hourly/daily/weekly ingestion metrics.

Highlights

  • • Event-driven flows with RabbitMQ; outbox + retry/backoff; deduplication.
  • • Dual-write & shadow traffic with drift detection and safe comparisons.
  • • WebFlux on Kubernetes (probes, HPA); blue/green & canary via Ingress.
  • • Latency reduction via I/O tuning & back-pressure; standardized metrics & tracing.

My Role

  • • Development of WebFlux-based microservices
  • • Database design and creation
  • • Data migration / “data pumping” from the monolith
  • • Deployments and CI/CD pipelines
  • • Writing unit and integration tests
  • • Functional discussions with BA/Tech Lead and the team
↑ Back to cases

SolbegSoft / Helmes — Risk & Asset Protection

Protection software for automotive & property insurance. We adapted the product for a specific region: localization & compliance rules, integrations with regional systems, and configuration-driven feature toggles. In parallel, we migrated selected monolith parts to microservices and hardened QA/stability.

Regional adaptation

  • • Core product wrapped with a region pack: locale, currency, and compliance rules.
  • • Integration adapters for policy/claims/payments, with mapping & validation layers.
  • • Feature flags per region; rules engine changes for product/regulatory logic.
  • • Fewer production incidents after QA hardening and adapter isolation.

My Role

  • • Adapted out-of-the-box features for a specific region (rules, locales, UX).
  • • Built integration adapters (policy/claims), mapping & validation layers.
  • • Configuration-driven toggles (feature flags) and rules engine changes.
  • • Helped extract modules from the monolith to microservices (contracts, endpoints).
  • • Tests: unit/integration, contract tests for adapters.
  • • Deployment support and functional scope discussions with BA/Tech Lead.
↑ Back to cases

Automotive Group — Calendar Management System

Internal Google-style calendar for an automotive group: schedule and manage events with filters, tags, and priorities; speaker profiles (photos + talk details); multi-user views for departments.

Delivery model

  • • Client provided brand & full design system (Figma) — strict visual parity.
  • • Waterfall governance, but monthly demos with the client — feedback → iterative scope.
  • • Sole backend engineer in a 6-person team (1 BE + 5 FE).
  • • Built from scratch in ~10 months + 2 months production support.

My contributions

  • • API design (REST), authn/authz (LDAP), audit log.
  • • Recurring events, time-zones & DST correctness; ICS export.
  • • Priorities/tags/filters, speaker profiles (photo + talk details).
  • • CI/CD, Testcontainers, Flyway migrations; performance tuning and caching.
↑ Back to cases

Projects

Godel Platform — Security, Performance & Observability

2025–Present

Shared Kubernetes platform on AWS for several product teams (insurance domain). Work splits evenly between building backend services and keeping the platform reliable and secure: automated DAST on every release, performance tests that guided migration of the busiest services to more scalable approaches, and unified logging that cut time to trace production issues.

  • Java 21
  • Groovy
  • Spring Boot
  • Kafka
  • RabbitMQ
  • Kubernetes
  • Helm
  • FluxCD
  • Terraform
  • GitLab CI
  • AWS EKS
  • DAST
  • Gatling
  • Datadog
Case

Automotive Group — Calendar Management System

2021–2022

Internal Google-style calendar for an automotive group: schedule and manage events with filters, tags, and priorities; speaker profiles with photo + talk details; multi-user views for departments.

  • Java 11
  • Spring
  • Hibernate
  • Gradle
  • MS SQL
  • Flyway
  • RabbitMQ
  • Docker
  • GitLab CI
  • Testcontainers
  • JUnit
  • Mockito
  • Swagger/OpenAPI
Case

SolbegSoft / Helmes — Risk & Asset Protection

2021–2022

Protection software for automotive & property insurance. Migrated parts of a legacy monolith to microservices; delivered new features, QA hardening, and stability improvements.

  • Java 8
  • Spring
  • EclipseLink ORM
  • Maven
  • Liquibase
  • SOAP
  • XML (SAXIF)
  • Tomcat
  • ActiveMQ
  • Ruleset
  • JUnit
  • Mockito
Case

Shell Energy — Smart Metering Platform

2022–2024

Real-time metering for 2,200,000+ users. XML-only legacy monolith wrapped by adapters; traffic strangled to WebFlux microservices on Kubernetes. Observability via Prometheus/Grafana with RPS and hourly/daily/weekly ingestion metrics.

  • Java 17
  • Spring WebFlux
  • Spring Boot
  • RabbitMQ
  • PostgreSQL
  • Cassandra
  • Docker
  • Kubernetes
  • Prometheus
  • Grafana
  • Liquibase
  • Testcontainers
  • JUnit
  • Mockito
  • Cucumber
  • REST
  • XML
  • Maven
  • Git
Case

Control-Plane: Event-driven tenant workflows

2024–2025

Internal multi-tenant control-plane for a SaaS platform: orchestrates full tenant lifecycle (onboarding, configuration, offboarding) using AWS Step Functions + Lambda. Implemented saga patterns with compensations, idempotency, fan-out/fan-in, and async SQS jobs. Built observability with CloudWatch/X-Ray and fault-tolerant retries. Achieved −25% Lambda cost and −30% p95 latency through provisioned concurrency, batching, and payload optimizations.

  • Java 21
  • Spring Boot
  • AWS Lambda
  • Step Functions
  • EventBridge
  • DynamoDB
  • SQS
  • Terraform
  • X-Ray
Case

Contact

Warsaw, Poland · B2B · Contract · Remote/Hybrid/On-site

k30medvedev@gmail.com +48 698 567 174 GitHub LinkedIn