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Career Plan — Lead Data Architect → Director → VP

> Everything is TITAN-managed: websites, business ideas, and the job search.

> This plan moves Harnoor to higher-paid executive roles by closing hands-on

> skill gaps and packaging proof into an organized POC portfolio.

The target

• Trajectory: Senior / Lead Data Architect → Director, Data & AI → VP
• Comp: $250k+ base now → total-comp executive band
• Hands-on stack to own: Databricks · Kafka · Agentic AI apps · RAG · LLM
• Constraints (from memory): US Green Card holder (no sponsorship), direct

companies only, skip federal/clearance roles.

The honest skills audit

Harnoor is not starting from zero — TITAN itself is the proof. The gaps are

narrow and the rest just needs packaging.

| Skill | Status | Evidence / gap |

|-------|--------|----------------|

| Agentic AI apps | ✅ strong | The Foundry — 6 incubated agentic apps; TITAN's 6-agent system |

| RAG | ✅ strong | titan-search server-side index, newsletter archive, Bedrock KB plans |

| LLM orchestration | ✅ strong | TITAN: multi-agent, Bedrock Sonnet, prompt-caching, fallbacks |

| Cloud / serverless architecture | ✅ strong | 60+ CloudFront dists, Lambda, S3, DDB, EventBridge in production |

| Databricks | ❌ gap | No hands-on lakehouse work — the #1 thing to close |

| Kafka / streaming | ⚠️ partial | Event-driven via EventBridge; no true Kafka/stream-processing POC |

| Executive packaging | ⚠️ gap | Work is real but not framed as architecture case studies |

Reframe: the job is 80% packaging what already exists + 20% *closing two

genuine hands-on gaps (Databricks, Kafka)*.

The organized POC portfolio

Five proof-of-concept projects. Each = a GitHub repo + a 1-page architecture

brief + a live demo link. Together they are the portfolio an executive hiring

panel needs to see.

| # | POC | Demonstrates | Source |

|---|-----|--------------|--------|

| 1 | Databricks Lakehouse — medallion (bronze/silver/gold), Delta Lake, Unity Catalog, a notebook pipeline | Databricks (the gap) | new build |

| 2 | Kafka Streaming Pipeline — Confluent Cloud / MSK ingest → stream processing → sink, exactly-once | Kafka / streaming (the gap) | new build |

| 3 | Agentic AI Platform — TITAN + The Foundry, written up as a system-design case study | Agentic AI, multi-agent | ✅ exists — package |

| 4 | Enterprise RAG — titan-search + archive RAG, written up with retrieval/eval metrics | RAG | ✅ exists — package |

| 5 | LLM Orchestration at Scale — Bedrock multi-model, caching, cost control, observability | LLM platform engineering | ✅ exists — package |

Phased timeline

Phase 1 — Close gaps + package (0–3 months) → land Lead / Principal Architect

• Databricks Certified Data Engineer Associate (cert) + build POC #1
• Confluent Kafka Developer cert + build POC #2
• Write the 3 case studies (POC 3–5) from existing TITAN work
• Rebuild resume + LinkedIn around "AI/Data Architect who ships" with the portfolio
• Apply only to direct-company Lead/Principal/Staff Architect roles

Phase 2 — Perform + get visible (3–12 months) → Director, Data & AI

• In-role: lead a flagship initiative end-to-end; mentor; own an architecture review board
• External: 1 conference talk or published architecture write-up per quarter
• Target the internal Director promotion or lateral Director move

Phase 3 — Org leadership (12–24 months) → VP

• Own a roadmap + budget; build/lead a team; show P&L or cost-impact numbers
• Executive narrative: "scaled AI/data org from X to Y"

Certifications (priority order)

1. Databricks Certified Data Engineer Associate — closes the #1 gap

2. Confluent Certified Developer for Apache Kafka

3. (Optional) Databricks Data Engineer Professional · AWS ML Specialty

How TITAN / Genius tracks this

• A Career panel in Genius — target role, the 5 POCs with status, next cert
• POC repos live under F:/TITAN/files/career-pocs/ → S3-backed
• The existing job-search-daily-digest feeds the digest; sharpen its ICP to

"Lead/Principal/Director Data & AI Architect, direct companies"

• Learn-loop (FRIDAY stage 8): the solo-founder / AI YouTube ingest doubles as

exec-skill input

Next action

Build POC #1 (Databricks Lakehouse) — it closes the single biggest gap and is

the fastest credibility win.

Change log

• 2026-05-17: created — career skill-enhancement plan, TITAN-managed.