System Management Console for CloudTV
Revolutionizing Cloud-Native TV Infrastructure Management for Charter Communications.
Role: Senior Software Engineer
Tech Stack: React, Node.js, MongoDB, InfluxDB, Docker, AWS, Bull Queue, Ansible
Observability
AWS
Overview
The SMC centralizes control over distributed deployments of CloudTV components across multiple geographic sites. It automated lifecycle operations for cloud-based TV systems, reducing deployment time from 40 hours per site to 2 hours, simultaneous deployments across sites, while also enabling real-time monitoring of Millions of concurrent streaming sessions. It enabled real-time health monitoring, and ensured high availability for Charter’s nationwide cable TV services.
Overview
The System Management Console (SMC) is a robust, cloud-native management and observability platform designed to streamline the monitoring, deployment and operational support of ActiveVideo’s CloudTV platform infrastructure - abstracting complex infrastructure into a single-pane dashboard and cutting down deployment cycles dramatically.
The CloudTV platform (by ActiveVideo) virtualizes set-top box capabilities — rendering, UI logic, and media composition—by streaming fully rendered UIs from the cloud —transforming legacy TVs into smart, cloud-powered experiences. This allows MSOs (Managed Service Operators) like Charter Communications(now Spectrum) to deliver feature-rich interfaces without hardware upgrades, while managing complex distributed infrastructure across regions.
Core Services of CloudTV
Component | Description | SMC Integration |
CSM (Central Session Manager) | Manages session lifecycle and stream routing across sites | Health polling, load visualization, version tracking |
Stitcher | Renders and stitches UI/video streams for end users | CPU, memory, and error-state monitoring; alerts |
Scaler & Funnel | Optimize ingress traffic and manage service capacity | Site-level diagnostics and flow integrity tracking |
Transcoder | Converts video streams into adaptive formats based on bandwidth/device | Monitored usage, health, and real-time throughput stats |
PSM (Provisioning & Scale Manager) | Orchestrates new service rollouts and scaling operations | Deployment automation and state syncing |
Data Collector | Aggregates telemetry, logs, and session KPIs | Backend for analytics and alert systems |
Project Details
Key Challenges
Heterogeneous Infrastructure: Multi-site deployments had varying hardware configurations (e.g., Stitcher:GX nodes with Intel Quick Sync vs. legacy servers).
Operationally complex: With numerous regional data centres, each running key CloudTV components. Site deployment and monitoring previously required 40+ manual hours per site, terminal-based workflows with minimal visibility and siloed logs and no consolidated operational intelligence, which resulted in high operational costs, fragmented monitoring, and delayed resolution of outages or degradations.
Real-Time Data Processing: Monitoring 2 million+ concurrent sessions required low-latency metric aggregation.
High Availability: Ensuring 99.99% uptime during CSM cluster upgrades
Key Objectives
Centralized Management: Unify control of multi-site CloudTV deployments.
Observability: Visualize system health & user sessions (CPU, RAM, bandwidth, session errors) via dashboards.
Automation: Enable single-click upgrades, blue-green deployments, and infrastructure discovery.
Scalability: Support dynamic scaling of CloudTV components (e.g., Stitcher clusters) based on demand.
Key Features
Centralized Multi-Site Dashboard
Site Health Overview: Live status of individual component infrastructure (CSMs, Stitchers, Scalers, Transcoders) across multiple sites like Reno, NV, and St. Louis, MO.
Component-Level Insights: Drill down into individual components (Stitcher, CSM, PSM) to view CPU, memory, and bandwidth utilization using SNMP.
Session Monitoring: Visualize millions of session details, activity and errors for faster resolution.
Automated Deployment
Infrastructure Discovery: Parse configuration files (XML) to auto-detect CloudTV components at new sites.
Ansible Playbooks: Deploy Stitcher clusters, CSM, and Scaler Nodes with pre-validated templates.
Blue-Green Deployments: Ensure zero downtime during upgrades by routing traffic to standby nodes.
Real-Time Monitoring & Alerts
Threshold-Based Alerts: Trigger notifications (Slack, Email) for anomalies like API errors, thresholds for CPU, Memory and service failure.
Capacity Planning: Predict resource requirements using historical metrics (e.g., session growth trends).
Single Click upgrades
Version Rollouts: Apply patches or feature updates across all Stitchers in parallel.
Rollback Mechanism: Revert to previous states if deployments fail.
Multi-Tenant Security
Enforced RBAC with Okta SSO, ensuring Charter’s operations team could only access designated sites.
Tech Stack
Frontend: React with Redux for state management, D3.js for interactive dashboards.
Backend: Node.js microservices with Bullqueue for job queues.
WebSockets for live session updates, SNMP for health data(cpu, ram, memory).
Data Layer: MongoDB for app data (sites, users), InfluxDB for time-series metrics.
Infrastructure: AWS EC2, S3 for Ansible Playbook storage, Jenkins for CI/CD pipelines.
Ansible for deployment automation.
ELK Stack for logs.
Meilisearch for Search.
Integration with CloudTV Components
CSM (Central Session Manager): The SMC interfaces with the CSM to monitor session states, allocate QAM bandwidth, and manage failover.
Stitcher: Real-time tracking of Stitcher clusters (e.g., CPU/memory usage, video encoding latency) via custom APIs.
Scaler/Transcoder: Automated scaling of Scaler Nodes based on live video ingestion load.
Results & Business Impact
95% Reduction in Deployment Time (40 hours → 2 hours)
$2.5M+ Annual Cost Savings across Charter’s infrastructure operations
30+ Regional Sites Managed through centralized tooling
99.99% uptime achieved during Zero Downtime Rollouts with blue-green deployments
Faster Incident Resolution via observability and alerting features
Empowered Ops Teams to proactively manage and scale deployments
My Contribution
As a Senior Software Engineer, I was an integral part from the beginning of the project to the delivery. This project was a long one which was executed over a period of over 2 years.
Engineering Execution
API Development & Integration:
RESTful API Design: Built a Node.js/Express.js backend to unify interactions with CloudTV components (CSM, Stitcher, PSM). APIs enabled real-time health checks, session management, and deployment triggers.
SNMP Integration: Engineered SNMP polling for hardware monitoring (e.g., CMTS/QAM devices), translating raw metrics (CPU, bandwidth) into structured JSON for dashboards.
Created Insightful Frontend Dashboards:
Developed responsive, real-time dashboards using React, enabling ops teams to visualize system health, filter by site, and monitor key metrics (CPU, memory, bandwidth, and session errors) with minimal latency.
Integrated with InfluxDB for Time-Series Monitoring:
Connected backend services to InfluxDB to ingest, store, and query high-volume telemetry data, enabling historical trend analysis, anomaly detection, and visual charting of system metrics.
Enabled Infrastructure Monitoring with SNMP:
Built polling modules to interface with networked devices via SNMP, allowing the system to collect device health, uptime, and port metrics from components like Stitchers, CSMs, and Transcoders.
Automated Deployments with Ansible:
Authored scalable Ansible playbooks for site provisioning, upgrade automation, and rollback support—achieving zero-downtime deployments and reducing rollout cycles from 40 hours to 2.
Playbook Development: Wrote modular Ansible roles to deploy Stitcher:GX clusters, CSM, and Scaler Nodes across AWS/Azure.
Dynamic Inventory Scripts: Built Python scripts to auto-generate Ansible inventories from customer-provided configs, reducing manual errors by 90%.
Blue-Green Deployments: Automated zero-downtime upgrades using Ansible to orchestrate load balancer rerouting and post-deployment validation.
Product & Strategy Impact
Led Discovery with Enterprise Stakeholders:
Ran collaborative workshops and discovery sessions with ActiveVideo and Charter Communications to capture pain points, align feature needs, and define operational success metrics.
Acted as Embedded Product Owner:
Shaped roadmap priorities by translating field insights into specs, aligning cross-functional teams, and driving decisions that directly improved platform adoption and uptime.
Aligned Metrics to Operational Outcomes:
Defined key health thresholds and alerting logic based on ops feedback, ensuring SNMP and InfluxDB data translated into actionable insights and reduced incident response times.
Enabled Multi-Site Observability at Scale:
Unified data from diverse sources into a single, consistent monitoring experience—empowering teams to proactively detect issues, automate response workflows, and optimize resource usage across regions.
Reflection
This role deepened my expertise in full-stack development while sharpening my ability to align technical solutions with business goals.
By wearing both “engineer” and “product owner” hats, I delivered a platform that not only solved technical challenges but also empowered customers like Charter to scale their operations confidently. The integration of SNMP and InfluxDB, in particular, became a cornerstone for proactive monitoring, proving that observability is as critical as functionality in enterprise systems.
More Works
FAQ
01
What kinds of projects have you managed?
02
What industries have you worked in?
03
What technical skills do you bring to the table?
04
What project management methodologies are you most familiar with?
05
Do you have experience managing remote or distributed teams?
06
How do you handle project risks and escalations?
07
What is your leadership style?
08
Are you open to freelance consulting / side projects ?
System Management Console for CloudTV
Revolutionizing Cloud-Native TV Infrastructure Management for Charter Communications.
Role: Senior Software Engineer
Tech Stack: React, Node.js, MongoDB, InfluxDB, Docker, AWS, Bull Queue, Ansible
Observability
AWS
Overview
The SMC centralizes control over distributed deployments of CloudTV components across multiple geographic sites. It automated lifecycle operations for cloud-based TV systems, reducing deployment time from 40 hours per site to 2 hours, simultaneous deployments across sites, while also enabling real-time monitoring of Millions of concurrent streaming sessions. It enabled real-time health monitoring, and ensured high availability for Charter’s nationwide cable TV services.
Overview
The System Management Console (SMC) is a robust, cloud-native management and observability platform designed to streamline the monitoring, deployment and operational support of ActiveVideo’s CloudTV platform infrastructure - abstracting complex infrastructure into a single-pane dashboard and cutting down deployment cycles dramatically.
The CloudTV platform (by ActiveVideo) virtualizes set-top box capabilities — rendering, UI logic, and media composition—by streaming fully rendered UIs from the cloud —transforming legacy TVs into smart, cloud-powered experiences. This allows MSOs (Managed Service Operators) like Charter Communications(now Spectrum) to deliver feature-rich interfaces without hardware upgrades, while managing complex distributed infrastructure across regions.
Core Services of CloudTV
Component | Description | SMC Integration |
CSM (Central Session Manager) | Manages session lifecycle and stream routing across sites | Health polling, load visualization, version tracking |
Stitcher | Renders and stitches UI/video streams for end users | CPU, memory, and error-state monitoring; alerts |
Scaler & Funnel | Optimize ingress traffic and manage service capacity | Site-level diagnostics and flow integrity tracking |
Transcoder | Converts video streams into adaptive formats based on bandwidth/device | Monitored usage, health, and real-time throughput stats |
PSM (Provisioning & Scale Manager) | Orchestrates new service rollouts and scaling operations | Deployment automation and state syncing |
Data Collector | Aggregates telemetry, logs, and session KPIs | Backend for analytics and alert systems |
Project Details
Key Challenges
Heterogeneous Infrastructure: Multi-site deployments had varying hardware configurations (e.g., Stitcher:GX nodes with Intel Quick Sync vs. legacy servers).
Operationally complex: With numerous regional data centres, each running key CloudTV components. Site deployment and monitoring previously required 40+ manual hours per site, terminal-based workflows with minimal visibility and siloed logs and no consolidated operational intelligence, which resulted in high operational costs, fragmented monitoring, and delayed resolution of outages or degradations.
Real-Time Data Processing: Monitoring 2 million+ concurrent sessions required low-latency metric aggregation.
High Availability: Ensuring 99.99% uptime during CSM cluster upgrades
Key Objectives
Centralized Management: Unify control of multi-site CloudTV deployments.
Observability: Visualize system health & user sessions (CPU, RAM, bandwidth, session errors) via dashboards.
Automation: Enable single-click upgrades, blue-green deployments, and infrastructure discovery.
Scalability: Support dynamic scaling of CloudTV components (e.g., Stitcher clusters) based on demand.
Key Features
Centralized Multi-Site Dashboard
Site Health Overview: Live status of individual component infrastructure (CSMs, Stitchers, Scalers, Transcoders) across multiple sites like Reno, NV, and St. Louis, MO.
Component-Level Insights: Drill down into individual components (Stitcher, CSM, PSM) to view CPU, memory, and bandwidth utilization using SNMP.
Session Monitoring: Visualize millions of session details, activity and errors for faster resolution.
Automated Deployment
Infrastructure Discovery: Parse configuration files (XML) to auto-detect CloudTV components at new sites.
Ansible Playbooks: Deploy Stitcher clusters, CSM, and Scaler Nodes with pre-validated templates.
Blue-Green Deployments: Ensure zero downtime during upgrades by routing traffic to standby nodes.
Real-Time Monitoring & Alerts
Threshold-Based Alerts: Trigger notifications (Slack, Email) for anomalies like API errors, thresholds for CPU, Memory and service failure.
Capacity Planning: Predict resource requirements using historical metrics (e.g., session growth trends).
Single Click upgrades
Version Rollouts: Apply patches or feature updates across all Stitchers in parallel.
Rollback Mechanism: Revert to previous states if deployments fail.
Multi-Tenant Security
Enforced RBAC with Okta SSO, ensuring Charter’s operations team could only access designated sites.
Tech Stack
Frontend: React with Redux for state management, D3.js for interactive dashboards.
Backend: Node.js microservices with Bullqueue for job queues.
WebSockets for live session updates, SNMP for health data(cpu, ram, memory).
Data Layer: MongoDB for app data (sites, users), InfluxDB for time-series metrics.
Infrastructure: AWS EC2, S3 for Ansible Playbook storage, Jenkins for CI/CD pipelines.
Ansible for deployment automation.
ELK Stack for logs.
Meilisearch for Search.
Integration with CloudTV Components
CSM (Central Session Manager): The SMC interfaces with the CSM to monitor session states, allocate QAM bandwidth, and manage failover.
Stitcher: Real-time tracking of Stitcher clusters (e.g., CPU/memory usage, video encoding latency) via custom APIs.
Scaler/Transcoder: Automated scaling of Scaler Nodes based on live video ingestion load.
Results & Business Impact
95% Reduction in Deployment Time (40 hours → 2 hours)
$2.5M+ Annual Cost Savings across Charter’s infrastructure operations
30+ Regional Sites Managed through centralized tooling
99.99% uptime achieved during Zero Downtime Rollouts with blue-green deployments
Faster Incident Resolution via observability and alerting features
Empowered Ops Teams to proactively manage and scale deployments
My Contribution
As a Senior Software Engineer, I was an integral part from the beginning of the project to the delivery. This project was a long one which was executed over a period of over 2 years.
Engineering Execution
API Development & Integration:
RESTful API Design: Built a Node.js/Express.js backend to unify interactions with CloudTV components (CSM, Stitcher, PSM). APIs enabled real-time health checks, session management, and deployment triggers.
SNMP Integration: Engineered SNMP polling for hardware monitoring (e.g., CMTS/QAM devices), translating raw metrics (CPU, bandwidth) into structured JSON for dashboards.
Created Insightful Frontend Dashboards:
Developed responsive, real-time dashboards using React, enabling ops teams to visualize system health, filter by site, and monitor key metrics (CPU, memory, bandwidth, and session errors) with minimal latency.
Integrated with InfluxDB for Time-Series Monitoring:
Connected backend services to InfluxDB to ingest, store, and query high-volume telemetry data, enabling historical trend analysis, anomaly detection, and visual charting of system metrics.
Enabled Infrastructure Monitoring with SNMP:
Built polling modules to interface with networked devices via SNMP, allowing the system to collect device health, uptime, and port metrics from components like Stitchers, CSMs, and Transcoders.
Automated Deployments with Ansible:
Authored scalable Ansible playbooks for site provisioning, upgrade automation, and rollback support—achieving zero-downtime deployments and reducing rollout cycles from 40 hours to 2.
Playbook Development: Wrote modular Ansible roles to deploy Stitcher:GX clusters, CSM, and Scaler Nodes across AWS/Azure.
Dynamic Inventory Scripts: Built Python scripts to auto-generate Ansible inventories from customer-provided configs, reducing manual errors by 90%.
Blue-Green Deployments: Automated zero-downtime upgrades using Ansible to orchestrate load balancer rerouting and post-deployment validation.
Product & Strategy Impact
Led Discovery with Enterprise Stakeholders:
Ran collaborative workshops and discovery sessions with ActiveVideo and Charter Communications to capture pain points, align feature needs, and define operational success metrics.
Acted as Embedded Product Owner:
Shaped roadmap priorities by translating field insights into specs, aligning cross-functional teams, and driving decisions that directly improved platform adoption and uptime.
Aligned Metrics to Operational Outcomes:
Defined key health thresholds and alerting logic based on ops feedback, ensuring SNMP and InfluxDB data translated into actionable insights and reduced incident response times.
Enabled Multi-Site Observability at Scale:
Unified data from diverse sources into a single, consistent monitoring experience—empowering teams to proactively detect issues, automate response workflows, and optimize resource usage across regions.
Reflection
This role deepened my expertise in full-stack development while sharpening my ability to align technical solutions with business goals.
By wearing both “engineer” and “product owner” hats, I delivered a platform that not only solved technical challenges but also empowered customers like Charter to scale their operations confidently. The integration of SNMP and InfluxDB, in particular, became a cornerstone for proactive monitoring, proving that observability is as critical as functionality in enterprise systems.
More Works
FAQ
01
What kinds of projects have you managed?
02
What industries have you worked in?
03
What technical skills do you bring to the table?
04
What project management methodologies are you most familiar with?
05
Do you have experience managing remote or distributed teams?
06
How do you handle project risks and escalations?
07
What is your leadership style?
08
Are you open to freelance consulting / side projects ?
System Management Console for CloudTV
Revolutionizing Cloud-Native TV Infrastructure Management for Charter Communications.
Role: Senior Software Engineer
Tech Stack: React, Node.js, MongoDB, InfluxDB, Docker, AWS, Bull Queue, Ansible
Observability
AWS
Overview
The SMC centralizes control over distributed deployments of CloudTV components across multiple geographic sites. It automated lifecycle operations for cloud-based TV systems, reducing deployment time from 40 hours per site to 2 hours, simultaneous deployments across sites, while also enabling real-time monitoring of Millions of concurrent streaming sessions. It enabled real-time health monitoring, and ensured high availability for Charter’s nationwide cable TV services.
Overview
The System Management Console (SMC) is a robust, cloud-native management and observability platform designed to streamline the monitoring, deployment and operational support of ActiveVideo’s CloudTV platform infrastructure - abstracting complex infrastructure into a single-pane dashboard and cutting down deployment cycles dramatically.
The CloudTV platform (by ActiveVideo) virtualizes set-top box capabilities — rendering, UI logic, and media composition—by streaming fully rendered UIs from the cloud —transforming legacy TVs into smart, cloud-powered experiences. This allows MSOs (Managed Service Operators) like Charter Communications(now Spectrum) to deliver feature-rich interfaces without hardware upgrades, while managing complex distributed infrastructure across regions.
Core Services of CloudTV
Component | Description | SMC Integration |
CSM (Central Session Manager) | Manages session lifecycle and stream routing across sites | Health polling, load visualization, version tracking |
Stitcher | Renders and stitches UI/video streams for end users | CPU, memory, and error-state monitoring; alerts |
Scaler & Funnel | Optimize ingress traffic and manage service capacity | Site-level diagnostics and flow integrity tracking |
Transcoder | Converts video streams into adaptive formats based on bandwidth/device | Monitored usage, health, and real-time throughput stats |
PSM (Provisioning & Scale Manager) | Orchestrates new service rollouts and scaling operations | Deployment automation and state syncing |
Data Collector | Aggregates telemetry, logs, and session KPIs | Backend for analytics and alert systems |
Project Details
Key Challenges
Heterogeneous Infrastructure: Multi-site deployments had varying hardware configurations (e.g., Stitcher:GX nodes with Intel Quick Sync vs. legacy servers).
Operationally complex: With numerous regional data centres, each running key CloudTV components. Site deployment and monitoring previously required 40+ manual hours per site, terminal-based workflows with minimal visibility and siloed logs and no consolidated operational intelligence, which resulted in high operational costs, fragmented monitoring, and delayed resolution of outages or degradations.
Real-Time Data Processing: Monitoring 2 million+ concurrent sessions required low-latency metric aggregation.
High Availability: Ensuring 99.99% uptime during CSM cluster upgrades
Key Objectives
Centralized Management: Unify control of multi-site CloudTV deployments.
Observability: Visualize system health & user sessions (CPU, RAM, bandwidth, session errors) via dashboards.
Automation: Enable single-click upgrades, blue-green deployments, and infrastructure discovery.
Scalability: Support dynamic scaling of CloudTV components (e.g., Stitcher clusters) based on demand.
Key Features
Centralized Multi-Site Dashboard
Site Health Overview: Live status of individual component infrastructure (CSMs, Stitchers, Scalers, Transcoders) across multiple sites like Reno, NV, and St. Louis, MO.
Component-Level Insights: Drill down into individual components (Stitcher, CSM, PSM) to view CPU, memory, and bandwidth utilization using SNMP.
Session Monitoring: Visualize millions of session details, activity and errors for faster resolution.
Automated Deployment
Infrastructure Discovery: Parse configuration files (XML) to auto-detect CloudTV components at new sites.
Ansible Playbooks: Deploy Stitcher clusters, CSM, and Scaler Nodes with pre-validated templates.
Blue-Green Deployments: Ensure zero downtime during upgrades by routing traffic to standby nodes.
Real-Time Monitoring & Alerts
Threshold-Based Alerts: Trigger notifications (Slack, Email) for anomalies like API errors, thresholds for CPU, Memory and service failure.
Capacity Planning: Predict resource requirements using historical metrics (e.g., session growth trends).
Single Click upgrades
Version Rollouts: Apply patches or feature updates across all Stitchers in parallel.
Rollback Mechanism: Revert to previous states if deployments fail.
Multi-Tenant Security
Enforced RBAC with Okta SSO, ensuring Charter’s operations team could only access designated sites.
Tech Stack
Frontend: React with Redux for state management, D3.js for interactive dashboards.
Backend: Node.js microservices with Bullqueue for job queues.
WebSockets for live session updates, SNMP for health data(cpu, ram, memory).
Data Layer: MongoDB for app data (sites, users), InfluxDB for time-series metrics.
Infrastructure: AWS EC2, S3 for Ansible Playbook storage, Jenkins for CI/CD pipelines.
Ansible for deployment automation.
ELK Stack for logs.
Meilisearch for Search.
Integration with CloudTV Components
CSM (Central Session Manager): The SMC interfaces with the CSM to monitor session states, allocate QAM bandwidth, and manage failover.
Stitcher: Real-time tracking of Stitcher clusters (e.g., CPU/memory usage, video encoding latency) via custom APIs.
Scaler/Transcoder: Automated scaling of Scaler Nodes based on live video ingestion load.
Results & Business Impact
95% Reduction in Deployment Time (40 hours → 2 hours)
$2.5M+ Annual Cost Savings across Charter’s infrastructure operations
30+ Regional Sites Managed through centralized tooling
99.99% uptime achieved during Zero Downtime Rollouts with blue-green deployments
Faster Incident Resolution via observability and alerting features
Empowered Ops Teams to proactively manage and scale deployments
My Contribution
As a Senior Software Engineer, I was an integral part from the beginning of the project to the delivery. This project was a long one which was executed over a period of over 2 years.
Engineering Execution
API Development & Integration:
RESTful API Design: Built a Node.js/Express.js backend to unify interactions with CloudTV components (CSM, Stitcher, PSM). APIs enabled real-time health checks, session management, and deployment triggers.
SNMP Integration: Engineered SNMP polling for hardware monitoring (e.g., CMTS/QAM devices), translating raw metrics (CPU, bandwidth) into structured JSON for dashboards.
Created Insightful Frontend Dashboards:
Developed responsive, real-time dashboards using React, enabling ops teams to visualize system health, filter by site, and monitor key metrics (CPU, memory, bandwidth, and session errors) with minimal latency.
Integrated with InfluxDB for Time-Series Monitoring:
Connected backend services to InfluxDB to ingest, store, and query high-volume telemetry data, enabling historical trend analysis, anomaly detection, and visual charting of system metrics.
Enabled Infrastructure Monitoring with SNMP:
Built polling modules to interface with networked devices via SNMP, allowing the system to collect device health, uptime, and port metrics from components like Stitchers, CSMs, and Transcoders.
Automated Deployments with Ansible:
Authored scalable Ansible playbooks for site provisioning, upgrade automation, and rollback support—achieving zero-downtime deployments and reducing rollout cycles from 40 hours to 2.
Playbook Development: Wrote modular Ansible roles to deploy Stitcher:GX clusters, CSM, and Scaler Nodes across AWS/Azure.
Dynamic Inventory Scripts: Built Python scripts to auto-generate Ansible inventories from customer-provided configs, reducing manual errors by 90%.
Blue-Green Deployments: Automated zero-downtime upgrades using Ansible to orchestrate load balancer rerouting and post-deployment validation.
Product & Strategy Impact
Led Discovery with Enterprise Stakeholders:
Ran collaborative workshops and discovery sessions with ActiveVideo and Charter Communications to capture pain points, align feature needs, and define operational success metrics.
Acted as Embedded Product Owner:
Shaped roadmap priorities by translating field insights into specs, aligning cross-functional teams, and driving decisions that directly improved platform adoption and uptime.
Aligned Metrics to Operational Outcomes:
Defined key health thresholds and alerting logic based on ops feedback, ensuring SNMP and InfluxDB data translated into actionable insights and reduced incident response times.
Enabled Multi-Site Observability at Scale:
Unified data from diverse sources into a single, consistent monitoring experience—empowering teams to proactively detect issues, automate response workflows, and optimize resource usage across regions.
Reflection
This role deepened my expertise in full-stack development while sharpening my ability to align technical solutions with business goals.
By wearing both “engineer” and “product owner” hats, I delivered a platform that not only solved technical challenges but also empowered customers like Charter to scale their operations confidently. The integration of SNMP and InfluxDB, in particular, became a cornerstone for proactive monitoring, proving that observability is as critical as functionality in enterprise systems.
More Works
FAQ
What kinds of projects have you managed?
What industries have you worked in?
What technical skills do you bring to the table?
What project management methodologies are you most familiar with?
Do you have experience managing remote or distributed teams?
How do you handle project risks and escalations?
What is your leadership style?
Are you open to freelance consulting / side projects ?