Andrei Nita

CTO who turns messy data orgs into investor-grade machines — 3× delivery, 60% cloud cost cut, 1→15 team built.

I've done this across VC-backed B2B SaaS companies from Series B to D — geospatial intelligence, AI platforms, subscription analytics. The pattern is consistent: fragmented tech, slow delivery, limited board visibility. The fix is always the same: clear architecture, the right data, and a team that executes reliably.

Andrei Nita — CTO
⚡ 3× Faster Engineering Delivery
💰 60% Lower Cloud Costs
👥 1→15 Team Size Scaled (Data & Analytics)
🤖 75% Manual Reporting Automation
📈 Series B–D Fundraising Supported (data rooms, board dashboards)

Previously at

McKenzie Intelligence Services · Builder.ai · Busuu · Oracle · IBM
🛠

CTO for Data and AI-driven SaaS

I build lean engineering orgs, data platforms, and AI products that grow ARR and improve unit economics — for VC-backed and subscription businesses from Seed to Series D.

Capabilities

🏗️

Architecture that scales without surprises

  • Design cloud-native systems that handle 10× load without emergency refactors (AWS, Azure)
  • Migrate monoliths to event-driven microservices without halting delivery
  • Build data warehouses that serve both operational queries and board reports (Snowflake, Redshift)
  • Ship CI/CD pipelines that make weekly releases the default, not the exception
📊

Data that earns board confidence

  • Build pipelines from raw → investor-grade metrics in weeks, not quarters (Airflow, Fivetran, Snowflake)
  • Automate the finance and product reporting that consumes analyst time (Tableau, Power BI, Domo)
  • Deliver ARR, MRR, churn, LTV, and CAC dashboards that hold up in a Series B–D data room
  • Replace fragmented Segment and warehouse setups with a unified, auditable data model
🤖

AI that ships to production, not just pilots

  • Take ML models from notebook to production with monitoring and feedback loops in place
  • Build knowledge graphs and NLP features that become product differentiators (Neo4j)
  • Define the data foundation AI actually needs — before the model work starts
  • Bridge data science and engineering so neither team blocks the other
⚙️

Engineering orgs that don't need rescuing

  • Build delivery processes where predictability replaces heroics
  • Cut cloud spend 40–60% by treating FinOps as an architecture discipline, not a cost exercise
  • Set up data governance and compliance that satisfies auditors without slowing the team
  • Translate technology roadmap into board language — and back again

Selected Impact

CTO — B2B Geospatial Intelligence SaaS

Context Subscription platform serving insurers, governments, and financial institutions.
Challenges Slow delivery, high cloud spend, limited board visibility on metrics.
What I did Redesigned delivery processes, rationalised cloud architecture, introduced data governance and management reporting.
Outcomes
  • 3× engineering delivery speed
  • 60% reduction in cloud costs while improving scalability and reliability
  • Governance and reporting framework used for board and investor updates

Director of Data & Analytics — VC-backed AI SaaS (Series B–D)

Context Hyper-growth AI SaaS scaling through Series B to D.
Challenges Fragmented data, manual reporting, limited visibility on subscription metrics.
What I did Built Data & Analytics org from 1 to 15, unified the data stack, led AI initiatives.
Outcomes
  • Board dashboards for ARR, MRR, churn, LTV, CAC, and forecasting — directly supporting Series B–D fundraising
  • 75% reduction in manual finance and product reporting effort
  • AI chatbot and knowledge graph capabilities launched in production

Foundations — Earlier Career

  • Senior Data Engineer — Busuu
  • Data Engineer — BBOXX
  • Middleware & Cloud Consultant — Oracle
  • Infrastructure Specialist — IBM
  • Electronics Engineer — Renault

Point of View

Unlocking the Power of Data Mesh

Medium • Featured

Data mesh solves the wrong problem if your governance isn't ready. Here's the honest readiness checklist.

First 90 Days

Days 0–30 — Understand and stabilise

  • Map current architecture, data flows, and delivery bottlenecks.
  • Meet customers, GTM, and finance to understand value levers and pain points.
  • Establish basic visibility: ARR, churn, funnel, and platform health dashboards.

Days 31–60 — Design and align

  • Define a lightweight technology and data roadmap tied to OKRs and commercial targets.
  • Set up a predictable delivery model (teams, rituals, metrics).
  • Prioritise quick wins (cloud cost cuts, key data fixes, or a focused AI feature).

Days 61–90 — Execute and scale

  • Start delivering high-impact roadmap items.
  • Formalise data governance (access, quality, security, reporting).
  • Agree ongoing cadence with leadership (monthly management reporting, quarterly roadmap review).

Contact

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⬇ Investor Resume ⬇ Technical Resume