DevOps/Cloud Engineer | Solutions Architect

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.