Application · Product Manager · Enterprise SaaS

Hi Christina — I'm Rahul.
A Product Manager built for enterprise SaaS.

10+ years of B2B / enterprise product management at IAG, Global Dairy Trade and beyond — shipping with engineering, measuring outcomes, and building inside compliance and data guardrails. This page is my case for the data-privacy SaaS PM role you're hiring for — mapped to the spec, line by line.

Read the case →Let's talk
10+ yrs
B2B / enterprise product
−75%
Cycle time on automated workflows
0 → 1
Shipped greenfield, repeatedly

The brief × the match

Your role, line by line. My evidence, side by side.

Requirements matched1/8
Evidence #1

10+ years owning enterprise-grade products end-to-end — IAG's digital claims platform across AU & NZ, and the marketplace platform at Global Dairy Trade (one of the world's largest commodity trading ecosystems).

10+
years PM on enterprise / B2B products

By the numbers

Shipped outcomes — not slideware.

Measurable product wins from the last few years across discovery, delivery, automation and AI — each one tied back to a JD line. Hover any chart for the detail.

−75%
Cycle time

On automatable workflows after shipping the decision engine.

+15%
Adoption shift

Share of users migrated to the new digital workflow.

+7%
Conversion lift

From the optimisation value stream on the core submission flow.

+3%
Revenue impact

Uplift attributable to the internal SME cross-sell product.

Cycle time, before vs after

Cycle time on automatable workflows, before vs after the decision engine shipped (IAG).

Automation

Automation coverage over time

Share of in-scope work resolved by automation vs manual handling.

AI · workflow

Adoption shift onto new workflow

Share of users migrated to the new digital workflow over time.

Adoption

Activation & auto-resolution funnel

Where inbound volume lands after activation, automation, and routing.

Activation

Conversion, optimised

+7% lift across the optimisation value stream on the core submission flow.

Optimisation

Revenue impact

Revenue uplift attributable to the internal SME cross-sell product.

Business case

Product lifecycle

How I run product end-to-end — discovery to outcome.

Click any stage. Each one shows the failure mode, the move I'd make, the tactics, and the outcome — grounded in work I've actually shipped.

01 · Discover

Customer & problem research

"We think we know what users want — but we're moving on hunches, not evidence."
— the team / customer
Where it breaks

Roadmaps shaped by HiPPOs and sales asks instead of validated problems; risk of building the wrong thing fast.

What I do

Stand up continuous discovery: weekly interviews, opportunity mapping, and a clear funnel from insight → bet.

Tactics
Weekly customer interviewsOpportunity solution treesJobs-to-be-done framingInsight → bet pipeline
Outcomes
Team effort
Discovery becomes a habit, not a project
Delivery quality
Bets backed by evidence, not opinion
Outcome / adoption
Roadmap shifts to outcomes the team believes in
Proof I've done this

McKinsey-led discovery work at IAG — produced a 3–5yr Customer Journey model still in use.

1 / 5

Career pulse

Each role, a beat on the same line.

  • Aug 2023 – Present
    Product Development Manager
    Global Dairy Trade
    • Leading a modular platform roadmap for a multi-sided B2B marketplace
    • Embedding evidence-based discovery and outcome metrics into delivery
  • May 2022 – Jul 2023
    Digital Product Manager
    Agency / Consulting
    • Ran discovery sprints + PRD practice to ship 0→1 products for clients
    • Coached teams on backlog hygiene, AC writing, and outcome measurement
  • Oct 2020 – May 2022
    Digital Product Manager
    IAG Group Tech & Ops
    • Owned product vision & roadmap for direct brands AU & NZ
    • Shipped the Real Time Decision Engine — 75% reduction in cycle time
    • Measured adoption + outcomes at exec cadence
  • Oct 2017 – Oct 2020
    Digital Product Manager
    IAG NZ
    • Integrated digital lodgement flow; +7% conversion via optimisation
    • Co-authored IAG's 3–5yr digital strategy with McKinsey
    • Ran IAG's first Digital Hack-Week — startup tempo inside enterprise
  • Aug 2013 – Sep 2015
    Digital Asset Manager
    IAG
    • Owned customer-facing product surfaces: websites, KB, web chat
    • Critical role moving renewals & payments to digital

Toolbelt

Your stack, honestly mapped.

Green dots are platforms or capabilities I've shipped on directly. Amber dots are adjacent — same problem space, transferable on day one.

Jira / Linear
Daily backlog, sprint, and roadmap ownership
Productboard
Roadmap + opportunity mapping for B2B SaaS
Figma
Co-creation with design, spec annotation, prototyping reviews
Amplitude / Mixpanel
Activation, retention and funnel analytics
SQL / data analytics
Self-serve queries on product + revenue data
Notion / Confluence
PRDs, ACs, decision logs, discovery write-ups
Miro
Discovery sprints, journey mapping, opportunity solution trees
A/B testing & experimentation
Optimisation value stream on core flows
OKRs & outcome roadmaps
Tied features to measurable business outcomes
Enterprise data / privacy context
Shipped inside IAG's compliance + data governance guardrails
Salesforce
Worked alongside Salesforce-based CX & sales teams
AI / LLM productisation
Early production automation; following the privacy + AI space closely
Direct experience Adjacent / transferable

Skills × requirements

Every requirement, mapped to the skills behind it.

Pick a requirement from the JD. The matrix shows the specific skills I've used to deliver it — green dots for direct experience, amber for adjacent.

JD requirement

5+ years' Product Management in B2B SaaS / enterprise software

10+ years owning enterprise products end-to-end — IAG's digital claims platform across AU & NZ, GDT's B2B marketplace, plus agency consulting on B2B SaaS clients.

Skills applied
Enterprise SaaS PMMulti-sided platformsCompliance-bound deliveryVendor & partner mgmt
10+ years PM on enterprise / B2B products
Direct experience Adjacent / transferable

Why this role

AI, privacy, and enterprise data — built with real ownership and pace.

The JD reads like the work I actively want to do — innovative AI and privacy products, real influence on strategy and operating practices, and high autonomy with experienced technical and commercial leaders.

Data privacy is one of the few enterprise spaces where product decisions sit right at the intersection of customer outcomes, engineering reality, and compliance. That's exactly the kind of problem I've shipped against before — and want to keep shipping against.

I'm hands-on by default: in standups with engineering, in discovery with customers, in the dashboards post-launch. Not a PM who hands off and disappears.

A high-growth company, broad scope across strategy → discovery → delivery → measurement, and the room to establish scalable product practices — that's the shape of role I'm optimising for next.

Let's talk

Christina — does this look like a fit?

Happy to jump on a quick call, send through a CV, or get this in front of the hiring team. Whatever's easiest.

Email RahulLinkedIn →

Built by hand for Christina @ Talent Army.