ENGINEERING WORK & CASE STUDIES

AdTech systems I've worked on, debugged and optimized.

Structured engineering case studies demonstrating real-world problem discovery, data pipeline architecture, diagnostic troubleshooting, and operational root-cause analysis (RCA) across programmatic bidding, ad serving, video streaming, and APIs.

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Live Diagnostic Workbench CASE-01

OpenRTB 2.5 & 2.6 Diagnostic Validator & Payload Inspector

⚠️ 1. Problem (What was happening?)

Programmatic buyers and SSPs silently drop bid requests due to subtle JSON schema violations (such as missing poddur in CTV pods, invalid ext.eids, or unencoded GDPR user.ext.consent strings), contributing to lost bid opportunities and unfilled inventory.

🎯 2. Context & Stakeholders

SSPs, DSPs, Programmatic Publishers, and Integration Engineers.

3. Architecture Flow: Raw OpenRTB JSON → Schema Validation Engine → 20+ IAB Protocol Checks → Real-Time Diagnostics → Markdown Audit Export
🛠️ 4. Technical Approach & Implementation

Built an interactive, in-browser validation engine that evaluates real-time bid requests and responses against IAB specifications. Features 1-click test payloads (Display Banner, CTV Pod, Cookieless EIDs, Invalid Payload) and an instant clipboard export tool formatted for Jira and Slack tickets.

🔍 5. Diagnostics & Outcome

Identifies missing podding objects (podid, podseq), low tmax timeout thresholds (<50ms), malformed user consent strings, and invalid devicetype values.

OpenRTB 2.6 JSON Schema CTV Podding TCF v2.2 JavaScript AST
Engineering Takeaway: Proves deep mastery of real-time programmatic auction mechanics, privacy compliance encoding, and payload debugging.
⚡ Test OpenRTB Validator →
Waterfall Evaluation Engine CASE-02

GAM 360 Orchestration & Dynamic Waterfall Simulator

⚠️ 1. Problem (What was happening?)

Ad server delivery conflicts and revenue loss frequently stem from misunderstandings of line item priority hierarchy (Sponsorship 4 vs Standard 8 vs Price Priority 12 vs Open Bidding) and dynamic price floor competition.

🎯 2. Context & Stakeholders

Ad Operations Teams, Yield Managers, Ad Server Solutions Engineers, and Publisher Monetization Leads.

3. Architecture Flow: Ad Request Key-Values → Sponsorship Priority Check → Standard Delivery Evaluation → Dynamic Floor Competition → Highest Yield Winner
🛠️ 4. Technical Approach & Implementation

Built an interactive ad server simulation workbench allowing engineers to test priority overrides, simulate external header bidding CPM inputs, and inspect step-by-step waterfall decision paths in real time.

🔍 5. Diagnostics & Outcome

Demonstrates why a $6.00 Price Priority line item loses to an eligible $0.00 Sponsorship line item due to guaranteed reservations, and isolates floor price blockages.

Google Ad Manager Waterfall Mechanics Dynamic Allocation Price Floor Logic
Engineering Takeaway: Proves comprehensive operational knowledge of ad serving logic, priority rules, and yield management architecture.
📊 Run GAM Waterfall Simulation →
Streaming Architecture Testbed CASE-03

CTV Dynamic Ad Insertion (DAI) & SSAI Manifest Playground

⚠️ 1. Problem (What was happening?)

Server-Side Ad Insertion frequently introduces playback buffering, audio loudness jumps, aspect ratio stretching, and broken quartile tracking beacons across Connected TV devices.

🎯 2. Context & Stakeholders

Connected TV (CTV) Publishers, SSAI Solution Architects, Video Engineering Teams, and OTT Streaming Platforms.

3. Architecture Flow: Live HLS Stream → SCTE-35 Cue Detection → VAST/VMAP Ad Call → Transcoding & Normalization → #EXT-X-DISCONTINUITY Stitching → Client Playback
🛠️ 4. Technical Approach & Implementation

Constructed an interactive stream inspector that demonstrates synchronized video playback with ad segment timing graphs, SCTE-35 cue markers, and tracking beacon firing logs.

🔍 5. Diagnostics & Outcome

Verifies discontinuity tag placement, manifest timing accuracy, and client vs server beacon firing parity.

HLS / .m3u8 SCTE-35 VAST 4.2 SSAI Architecture Video Player API
Engineering Takeaway: Demonstrates end-to-end expertise in video ad protocols, SSAI stream stitching, and CTV playback troubleshooting.
📺 Inspect Manifest Playground →
Triage & Incident Management CASE-04

AdTech Troubleshooting & Root-Cause Analysis (RCA) Knowledge Base

⚠️ 1. Problem (What was happening?)

AdTech onboarding and client technical escalations require structured, reproducible root-cause analysis across tracking discrepancies, API timeouts, and protocol errors.

🎯 2. Context & Stakeholders

Solution Engineers, Technical Account Managers (TAMs), and Level 2/3 AdTech Support Engineers.

3. Architecture Flow: Reported Symptom → Network Log Parsing → Protocol Spec Analysis → Root-Cause Identification → Remediation SOP
🛠️ 4. Technical Approach & Implementation

Curated a comprehensive 100-question technical diagnostic repository with 10 deep incident case studies and 5 practical mock integration blueprints covering real-world AdTech problem solving.

🔍 5. Diagnostics & Outcome

Provides step-by-step playbooks for resolving VAST 303/405 error cascades, Prebid timeout tuning, and Sellers.json / SupplyChain verification.

Root-Cause Analysis Incident Triage Log Debugging VAST 100–901 Prebid.js
Engineering Takeaway: Demonstrates high-level technical communication, systematic incident triage capability, and client-facing engineering leadership.
📖 Explore RCA Knowledge Base →
API & Data Pipeline CASE-05

AdTech REST API, Python Telemetry & SQL Discrepancy Pipeline

⚠️ 1. Problem (What was happening?)

Modern AdTech operations require bridging disparate ad server logs, SSP bidstreams, and buyer reporting APIs to detect impression discrepancies and pacing anomalies before billing reconciliation SLA breaches occur.

🎯 2. Context & Stakeholders

Technical Specialists, Solutions Architects, and Partner Integration Engineers.

3. Architecture Flow: AdTech REST API → Database Store → Python/Pandas Client → SQL Discrepancy Engine → Automated Underperformance Alert
🛠️ 4. Technical Approach & Implementation

Constructed a secure, read-only REST API endpoint layer paired with a standalone Python client and an advanced SQL query library (CTEs, Window Functions) that calculates real-time CTR, eCPM, and multi-partner billing variances.

🔍 5. Diagnostics & Outcome

Isolates render abort drop-offs, VAST 405 codec error surges by player device, and automated underperforming campaign alerts (CTR < 0.5%).

REST APIs Python / Pandas SQL & CTEs Model Context Protocol (MCP) Telemetry
Engineering Takeaway: Proves hands-on engineering capabilities across REST APIs, Python data automation, SQL log analysis, and Model Context Protocol (MCP) AI integration.
🔌 Explore APIs & Data Pipelines →
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