Product Manager & BI Developer (Data & AI Products)

Talent Spot Group; Manila; On-site; PHP 1,000,000 - 1,500,000; Full time

Job summary

Talent Spot Group is seeking a Product Manager who actually builds data and AI products—BI dashboards, internal AI apps, and lightweight tools. You’ll own the vision, design the experience, and ship it yourself using a modern toolchain, pulling in engineers when needed but driving the idea to a live URL.

You’ll translate vision into a roadmap, discover what to build by engaging internal users, and prototype interactions that scale from idea to production.

Qualifications

• Experience with data platforms, analytics tooling, or AI-native products.
• Background as a Developer, Designer, Analyst, or Data Scientist before moving into product management.
• Competent in TypeScript, Python, SQL.
• Familiarity with the modern data stack (warehouse, transformation, BI, orchestration) and LLM application patterns (RAG, agents, evals, guardrails).
• Experience launching products inside a non-tech-native company where adoption is harder than the build.

Responsibilities

• Set the vision and translate it into a 12–24 month roadmap.
• Discover what to build by interviewing internal customers and understanding workflows.
• Design the experience with wireframes and prototypes; move to clickable prototypes quickly.
• Build and ship products to live URL; manage Airtable backends and internal apps.
• Define and deliver BI dashboards; own metrics, definitions, and data model.
• Build internal AI-powered apps (copilots, agents, classifiers) that save time.
• Package prototypes for engineering when scaling; provide data and specs.
• Define success metrics; instrument and improve based on usage.
• Tell the roadmap and impact to leadership and the company.

Skills

Data platforms

Analytics tooling

AI-native products

TypeScript

Python

SQL

GitHub

Airtable

No-code platforms

Retool

Bubble

Glide

Tools

GitHub

Airtable

Retool

Bubble

Glide

Softr

Lovable

v0

Job description

Office of Data & Analytics | Reports to: VP, Data & Analytics

About the role

We're looking for a Product Manager who actually builds. You'll own the data and AI products that power Data & Analytics — BI dashboards, internal AI-powered apps, lightweight tools that solve real workflow pain — from the first sketch to the live URL.

You set the vision, design the experience, and ship it yourself using a modern toolchain (GitHub, Airtable, no-code platforms, AI-assisted development) — pulling in engineers when scale demands it, but never waiting on them to prove an idea works.

This is a role for a builder who thinks like a strategist, sketches like a designer, and ships like a founder.

What you'll do

Set the vision. Develop a clear, opinionated point of view on the data and AI products should build over the next 12–24 months. Translate that vision into a roadmap the team and stakeholders can rally around.

Discover what to build. Spend time with internal customers — operators, analysts, executives — to understand their workflows, their pain, and the decisions they're trying to make. Turn those insights into well-scoped product bets.

Design the experience. Sketch flows, build wireframes, and prototype interactions. Move from rough idea to clickable prototype in days, not weeks.

Build and ship. This is the core of the job. Take products from prototype to live, in-the-hands-of-users state — yourself. Stand up Airtable backends, ship internal apps with Retool / Bubble / Glide / Lovable / v0 / similar, push code to GitHub, deploy to a real URL, and own the production experience. Use AI coding assistants aggressively to compress what used to take an engineering sprint into an afternoon.

Ship BI products. Define and deliver dashboards that people actually open on a Monday morning. Own the metrics, the definitions, the underlying model, and the layout — because in BI, the UX is the product.

Ship AI products. Build internal AI-powered apps — copilots, agents, summarizers, classifiers — that demonstrably save time or unlock new capability. Make smart calls on build vs. buy and on where AI genuinely helps versus where it's a distraction.

Hand off thoughtfully. When something proves itself and needs to scale, package it cleanly for engineering — a working prototype, real usage data, and a clear spec. You make engineers' lives easier, not harder.

Measure and iterate. Define success metrics for every product you ship. Instrument, monitor, and improve based on real usage — not vibes.

Tell the story. Communicate roadmap, progress, and impact upward to leadership and outward to the company. Build the narrative that gets your work funded and adopted.

Building and shipping (core). You don't just spec products — you make them live. You're

hands-on with GitHub (commits, PRs, branches, basic CI), Airtable (schemas, automations,

interfaces), and at least one app builder (Retool, Bubble, Glide, Softr, Lovable, v0, or similar).

You ship working software, not slide decks of working software.

Vision and execution (core). You can sit in a strategy conversation with a VP and a scoping

conversation with an engineer in the same hour, and add value in both. The roadmap you write is the roadmap you build against — yourself.

UI/UX design and wireframing (core). You think in flows and screens. You can open Figma (or

your tool of choice) and produce wireframes, lo-fi mocks, and interactive prototypes that align

stakeholders before build. You understand information architecture, visual hierarchy, and the

difference between a dashboard that informs and one that overwhelms.

AI-native workflow. You use Claude, Cursor, Copilot, or similar tools as core parts of how you

build. You're comfortable wiring up an LLM to a database, prompting your way to a working feature, and knowing when to stop prompting and write the thing properly.

Data fluency. You're comfortable in the weeds: reading and writing SQL, designing a schema,

sanity-checking a metric definition, understanding why two dashboards disagree.

Storytelling and writing. You write crisp PRDs, memos, and launch narratives. You can build a

deck that gets a skeptical exec to fund the next quarter of work.

Customer obsession (internal). You treat internal users with the same care a consumer PM gives external customers. Adoption is your problem, not someone else's.

Bias to action. You'd rather ship a focused v1 this week and learn, than spec the perfect v3 for six months. You see "just build it" as a feature, not a hack.

The Desired Skills:

• Experienced in data platforms, analytics tooling, or AI-native products.
• Background as a Developer, Designer, Analyst, or Data Scientist before moving into product. management
• Competent in TypeScript / Python / SQL
• Familiarity with the modern data stack (warehouse, transformation, BI, orchestration) and with LLM application patterns (RAG, agents, evals, guardrails).
• Experience launching products inside a non-tech-native company, where adoption is harder than the build.