Matt CalalayCLOUD DEVOPS

DevOps Engineer

Cloud DevOps Engineer. I turn manual, fragile delivery into automated, observable pipelines defined as code, across AWS, GCP, and self-hosted setups.

Right now I'm building CI/CD, ChatOps, observability, and secrets platforms at a product company, and running my own self-hosted automation stack on the side.

Get in touch
01 — Toolkit

Skills & stack

  • Terraform
  • Ansible
  • GitLab
  • Docker
  • Harbor
  • Prometheus
  • Grafana
  • Loki
  • Fluent Bit
  • HashiCorp Vault
  • n8n
  • CI/CD
  • Claude

Cloud

  • AWS
  • Lambda
  • S3
  • EventBridge
  • Step Functions
  • SNS
  • Redshift
  • Athena
  • SageMaker
  • CodePipeline
  • EKS
  • CloudWatch
  • GCP
  • BigQuery
  • Pub/Sub

IaC & Automation

  • Terraform
  • Ansible
  • GitLab CI/CD
  • ChatOps
  • n8n

Containers & Orchestration

  • Docker
  • Kubernetes (EKS)
  • Harbor

Observability

  • Prometheus
  • Grafana
  • Loki
  • Fluentbit
  • Node Exporter
  • cAdvisor

Data

  • ETL pipelines
  • SQL
  • Apache Airflow
  • Informatica

Languages

  • Python
  • Bash
  • SQL
  • Linux
02 — Experience

The track record

  1. May 2026 – present

    Automations Specialist (self-directed)

    Independent

    I design and run a self-hosted automation stack on n8n and Claude, open-source and self-hosted by choice. This portfolio runs on it.

  2. Sep 2024 – present

    Cloud DevOps Engineer

    ShipERP

    My first DevOps role at a product company. I joined as a Junior and was promoted to Mid-level in 2026. The work: GitLab CI/CD pipelines, ChatOps deployments, centralized logging and monitoring, HashiCorp Vault secrets management, and Kubernetes/EKS operations.

  3. Jan 2024 – May 2024

    Data Engineering Senior Analyst

    Accenture, for a major food manufacturer

    Led a GCP and BigQuery migration that moved a legacy on-prem Oracle data warehouse to a cloud-native analytics platform.

  4. Nov 2022 – Mar 2024

    Data Engineering Analyst / DevOps Engineer

    Accenture, for a global technology enterprise

    Scaled and maintained AWS data pipelines, owned production incidents end to end, and led the 2023 security container upgrades.

  5. May 2021 – Nov 2022

    Associate Software Engineer / Data Platform Engineer

    Accenture, for a global technology enterprise

    Built an AWS data platform with Terraform, serverless architecture, and CI/CD pipelines for a large enterprise client.

03 — Selected work

Things I've shipped

GitLab CI/CD Pipeline Suite

Problem
Multiple components needed consistent, repeatable build-to-deploy pipelines across dev and production environments, without manual deploy steps or credential sprawl.
Approach
Designed full GitLab CI/CD pipelines that handle build, pytest, image tagging, artifact management, SSH-based deployment, and rollback. They pull credentials from Vault and run the same way across three separate components.
Stack
GitLab CI/CD, Docker, Vault, SSH, pytest
Outcome
My biggest deliverable here: a repeatable, auditable deploy process across dev and prod for every component, with the manual release steps gone.

ChatOps Deployment System

Problem
To trigger a deployment, you had to jump into the CI tooling directly, which slowed down how the team coordinated releases.
Approach
Built a chat-triggered deployment system that kicks off GitLab CI/CD jobs directly from chat commands.
Stack
GitLab CI/CD, ChatOps tooling
Outcome
Faster, more visible deployments with the whole team able to see and trigger releases without leaving chat.

Centralized Logging & Monitoring

Problem
Host and container metrics and logs were scattered, so diagnosing an incident was slow and alerting was inconsistent.
Approach
Stood up a centralized observability stack: Fluentbit shipping logs into Loki, visualized in Grafana, paired with Prometheus, Node Exporter, and cAdvisor for host and container metrics, with alerting on top.
Stack
Fluentbit, Loki, Grafana, Prometheus, Node Exporter, cAdvisor
Outcome
One place to see logs and metrics across hosts and containers, with alerts that warn the team before something breaks instead of after.

AWS Serverless Data Platform

Problem
An enterprise client needed a scalable, decoupled data platform that could ingest and serve analytics without anyone managing servers.
Approach
Authored Terraform IaC provisioning a fully serverless architecture: Lambda for compute, S3 for storage, EventBridge and Step Functions for orchestration, SNS for messaging, with Redshift and Athena for analytics and SageMaker for ML, deployed through CodeBuild/CodePipeline.
Stack
Terraform, AWS Lambda, S3, EventBridge, Step Functions, SNS, Redshift, Athena, SageMaker, CodeBuild/CodePipeline
Outcome
A decoupled, infrastructure-as-code data platform that scaled with demand and removed manual provisioning entirely.

HashiCorp Vault Secrets Platform

Problem
Secrets were managed inconsistently across pipelines and services, creating risk and operational overhead.
Approach
I proposed this one myself and built a central secrets platform on HashiCorp Vault, then wired it into the existing CI/CD pipelines.
Stack
HashiCorp Vault
Outcome
One auditable place for secrets across pipelines, with hardcoded credentials taken out of the deploy workflows.

Self-Hosted Automation Stack

Problem
I didn't want to run my own infrastructure and automation on expensive SaaS tools that lock you in. That's not how I like to work.
Approach
Built a self-hosted automation stack: n8n for workflow orchestration, Docker for containers, and Cloudflare tunnels for external access without opening any ports.
Stack
n8n, Docker, Cloudflare Tunnels
Outcome
The infrastructure this portfolio runs on. It's self-hosted, automated, and entirely mine.
04 — Credentials

Certifications

  • GCP Associate Cloud Engineer
    2021
  • AWS Cloud Practitioner
    2021
  • AWS Solutions Architect – Associate
    2022
  • GCP Professional Cloud DevOps Engineer
    2022
05 — About

I build it,
then I run it

I started in Electronics Engineering at Mapúa University, not cloud. That's where I learned power electronics and how systems fit together, finished in the top 10 of my ECE batch, and co-authored an IEEE machine learning paper on detecting mango pulp weevils. The engineering habit stuck with me: measure things and prove they work before you trust them. I bring the same approach to infrastructure.

Data engineering came first. I built AWS data platforms with Terraform and serverless architectures for big enterprise clients, then ran them in production, where I handled incidents, led the container security upgrades, and migrated a legacy on-prem Oracle warehouse onto GCP and BigQuery. Operating what I'd built is what pulled me toward DevOps. Shipping a pipeline once was never the hard part. Keeping it reliable and observable every day after was.

These days I'm a Cloud DevOps Engineer at a product company, building CI/CD pipelines, ChatOps workflows, centralized logging and monitoring, and secrets management. Off the clock, I run a self-hosted automation stack on my own hardware, because I'd rather understand and own my tools than rent someone else's.

  • I learn on my own. I worked through a run of cloud certifications and built side projects like a secrets platform from scratch.
  • I automate repetitive work and define infrastructure as code so it runs the same way every time.
  • I own incidents end to end, including the ones that hit off-hours and on holidays.
  • I prefer open-source, self-hosted tools over expensive platforms that lock you in.
  • I bring the rigor from my Electronics Engineering background to how I run cloud systems.
06 — Contact

Let's talk

Got a project, role, or question? Send it below and it comes straight to me. There's no email address to scrape here, just this form.

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