Product-Oriented Technical Operations Leader with 10+ years operating at the intersection of Support, Engineering, and Product within cloud-based data platforms.

I translate production failure patterns and enterprise escalations into structured product improvements. I care about how systems actually behave in the real world, not just how they look in architecture diagrams.

I operate in the "messy middle" between customers and code:

• 🔍 Pattern Recognition – Spotting recurring failure modes across 1000+ customers and converting them into reliability initiatives
• 🛠 Product Thinking – Translating support escalations into actionable roadmap items that prevent future issues
• 📊 Data-Driven Decisions – Using production telemetry, MTTR trends, and customer impact data to prioritize engineering work
• 🌉 Bridge Building – Aligning Support, SRE, Product, and Engineering teams around shared reliability goals

TL;DR: I turn "why did this break?" into "how do we make sure it never breaks again?"

• Distributed data pipelines (ingestion, transformation, destinations)
• Source connector reliability (PostgreSQL, MySQL, SaaS APIs)
• CDC, schema evolution, offset management
• Handling real-world edge cases: rate limits, network partitions, zombie connections

• 24x7 global support operations for cloud ELT systems
• P0/P1 production incident command & resolution
• SLA/MTTR optimization through automation and process improvements
• Enterprise escalation management

• AI-assisted ticket classification & RCA extraction
• Operational dashboards & observability improvements
• Knowledge base deflection strategies
• Self-service diagnostic tools

• Documentation, code snippets, utilities
• Real-world debugging scenarios
• Lessons from production incidents

• Python – automation, APIs, data processing, PySpark
• SQL – PostgreSQL, MySQL, Snowflake, Redshift, BigQuery
• Bash – scripting, operational glue, incident response

• Databases: PostgreSQL, MySQL, Snowflake, Redshift
• Streaming: Kafka, Debezium, CDC patterns
• Cloud Platforms: AWS, GCP, Azure environments
• ELT Tools: Experience debugging distributed ingestion systems

• REST APIs, OAuth flows, webhook systems
• Rate limiting, pagination, retry strategies
• Third-party connector troubleshooting (20+ integrations)

• Docker, CI/CD pipelines
• Incident management frameworks
• RCA documentation & post-mortem culture
• Observability & monitoring strategies

• Automated ticket classification using LLMs
• RCA pattern extraction across 10,000+ production incidents
• Integration with Google Sheets for stakeholder reporting

• Source connector edge case handling (auth failures, schema drift, CDC lag)
• Data consistency validation across sources and destinations
• API rate limit & network timeout resilience patterns

• Ticket summarization & issue categorization
• Focus on deterministic outputs and guardrails (not "magic")
• Reducing support engineer toil through intelligent automation

• Production debugging: timeouts, data loss, retry storms
• Root-cause analysis over symptom firefighting
• Implementing preventive measures based on failure patterns

Simple > Clever
Observability before optimization
Evidence over hype
Root causes over symptoms
Pragmatism over perfection

Core Beliefs:

• Systems fail in ways you didn't anticipate. Plan for it.
• The best feature is the one that prevents customer pain.
• Support engineers see patterns product teams don't. Listen to them.
• Reliability is a product feature, not just an SRE concern.
• Good documentation prevents more incidents than good code.

• 🌐 Website: legolasan.in
• 💼 LinkedIn: linkedin.com/in/arunsunderraj
• 📧 Email: arunsunderraj@outlook.com
• 📍 Location: Bengaluru, India