Vandana Pal

DevOps Engineer

Bengaluru, Karnataka, India4 yrs 5 mos experience

Most Likely To Switch

Key Highlights

Expert in CI/CD automation and cloud operations.
Proven track record in ETL workflow migration.
Strong scripting skills in Python and Bash for automation.

Stackforce AI infers this person is a DevOps Engineer specializing in cloud solutions and automation in SaaS environments.

Contact

vandanapal150@gmail.com LinkedIn

Skills

Core Skills

Ci/cdCloud Solutions & ArchitectureData Migration & IntegrationInfrastructure Automation

Other Skills

CI/CD pipelinesDockerKubernetesPythonBashApache AirflowPostgreSQLTerraformAnsibleAzure MonitorJenkinsGitLab CI/CDGroovyREST APIsElasticsearch

About

DevOps Engineer with ~5 years of experience in CI/CD automation, cloud platform operations, and infrastructure-driven development on Azure. Strong in building and maintaining scalable systems using Docker, Kubernetes (Pods, Deployments), and CI/CD pipelines, with hands-on experience in Python and Bash scripting for automation. Experienced in designing and orchestrating ETL workflows using Apache Airflow and PostgreSQL. Focused on improving system reliability, observability, and cost efficiency through monitoring, troubleshooting, and automation in production environments.

Experience

4 yrs 5 mos

Total Experience

2 yrs 2 mos

Average Tenure

2 yrs 7 mos

Current Experience

Intel corporation

Cloud Software Development Engineer

Oct 2023 – Present · 2 yrs 7 mos · Bengaluru · On-site

Built and supported CI/CD pipelines using Jenkins and GitLab CI/CD for automated build, validation, and deployment of applications and ETL workflows across environments.
Worked with Docker-based containerization and supported deployments using Kubernetes (Pods, Deployments, ReplicaSets) and Helm charts, ensuring consistent and reliable releases.
Troubleshot application, container, and pipeline failures using logs, monitoring tools, and resource analysis (CPU/memory), improving system reliability and reducing downtime.
Developed and used Python and Bash scripts for automation, monitoring, and operational tasks, improving efficiency and reducing manual intervention.
Led migration of ETL workflows from Azure Data Factory and Databricks to Apache Airflow and PostgreSQL, redesigning pipelines for improved scalability, flexibility, and control.
Designed and maintained Airflow DAGs with scheduling, retry logic, dependency handling, SLA monitoring, and alerting to ensure fault-tolerant execution.
Worked on infrastructure and deployment automation concepts, with exposure to Terraform and Ansible for configuration and environment management in collaboration with platform teams.
Integrated Azure Active Directory (AAD) SSO with Kibana and internal systems, improving access control and reducing manual provisioning by 40%.
Built monitoring and cost optimization solutions using Python, Azure Monitor, and Log Analytics, achieving ~20% reduction in cloud costs.
Collaborated with cross-functional teams to design scalable, reusable, and reliable systems, aligning with DevOps and GitOps practices.

CI/CD pipelinesDockerKubernetesPythonBashApache Airflow+6

Anuta networks

2 roles

Software Engineer

Dec 2021 – Oct 2023 · 1 yr 10 mos

Built and maintained CI/CD and automation pipelines using Jenkins and Groovy, reducing manual provisioning effort by 60% and improving deployment consistency.
Automated infrastructure and network provisioning workflows using APIs, enabling multi-vendor orchestration and improving efficiency by 3x.
Developed automation scripts using Python, Bash, and Groovy to streamline provisioning, monitoring, and operational workflows.
Troubleshot CI/CD pipeline failures, deployment issues, and system-level problems, ensuring stability and reliability across environments.
Worked with infrastructure automation concepts, including configuration management and workflow orchestration, aligned with tools like Ansible.
Developed ServiceNow-integrated workflows using REST APIs for automated incident management and alerting, improving response time and reducing manual effort.
Implemented security and vulnerability scanning pipelines using Jenkins, OpenVAS, and Burp Suite, significantly reducing testing cycle time.
Improved backend processing using Python and centralized logging via Elasticsearch, reducing report latency by 70%.