Hi, I'm Abdul Mujahid

I design and automate reliable cloud, analytics, and DevOps platforms that turn operational complexity into scalable, observable, and repeatable engineering systems.

• 🔭 Building cloud-native analytics and DevOps solutions
• ☁️ Cloud: AWS | Azure | GCP
• 🛠️ DevOps: Docker | Kubernetes | Terraform | CI/CD | GitHub Actions | Jenkins
• 📊 Analytics: Python | SQL | ETL/ELT | Data Engineering | BI & Reporting
• 📈 Observability: Prometheus | Grafana | ELK/OpenSearch | Monitoring | Alerting
• 🐧 Platforms: Linux | Bash | Git | Networking | Infrastructure Automation
• 🎯 Focus: Reliability, automation, security, cost optimization, and developer experience

Senior-level engineering mindset across Analytics, Data Engineering, Cloud Infrastructure, and DevOps. I enjoy building production-ready systems with infrastructure as code, automated delivery pipelines, measurable observability, and data-driven operational insights.

Cloud & Infrastructure: AWS · Azure · GCP · Terraform

DevOps & Containers: Docker · Kubernetes · Helm · Jenkins · GitHub Actions · CI/CD

Data & Analytics: Python · SQL · ETL/ELT · Apache Airflow · Power BI

Observability: Prometheus · Grafana · Elastic/ELK · Monitoring · Alerting

Platforms: Linux · Bash · Git · Networking · Automation

• Cloud Infrastructure: secure, scalable environments using Terraform and cloud-native services
• CI/CD Platforms: automated build, test, security scanning, deployment, and release workflows
• Kubernetes Platforms: container orchestration, Helm deployments, scaling, health checks, and operational automation
• Analytics Pipelines: reliable ETL/ELT workflows, data validation, transformation, and reporting
• Observability: metrics, logs, dashboards, alerting, SLO-oriented operational visibility
• Automation: Python and Bash tooling that eliminates repetitive operational work
• Platform Engineering: reusable templates, standards, documentation, and developer self-service

Terraform-based multi-environment infrastructure with reusable modules, remote state, IAM controls, networking, and automated validation.

GitHub Actions pipeline covering linting, testing, container builds, security checks, artifact management, and deployment promotion.

A reference Kubernetes environment demonstrating deployments, services, ingress, autoscaling, health probes, configuration, secrets, and Helm.

Python + SQL data pipeline demonstrating ingestion, transformation, data quality checks, scheduling, observability, and analytics-ready outputs.

Prometheus + Grafana + centralized logging patterns for infrastructure and application monitoring, alerting, and operational dashboards.

Automate → Validate → Observe → Secure → Scale → Improve

I value:

• Infrastructure as Code over manual configuration
• Automation over repetitive operations
• Small, reviewable changes over risky releases
• Observability as a first-class production capability
• Security and least privilege by default
• Data quality and reproducibility
• Documentation that enables teams to operate independently

• Kubernetes platform engineering
• Advanced Terraform architecture
• Cloud security and FinOps
• Data engineering and analytics engineering
• GitHub Actions and DevSecOps
• SRE practices, observability, and reliability engineering
• Python automation and production tooling

I'm interested in Senior DevOps, Cloud, Analytics Engineering, Data Platform, Platform Engineering, and SRE opportunities where automation, cloud infrastructure, data, and reliability come together.

📫 GitHub: @AbdulMujahid-Devops

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