Turning Six Months of On-the-Job Learning into a Structured Product-Minded Playbook

timi-lanre — Thu, 19 Jun 2025 19:50:53 +0000

Hey Dev.to family 👋,

Timi here, and today kicks off what I'm calling my dual-track transformation, a six-month crash course to turn the hands-on product instinct I've picked up as a data engineer into formal product strategy know-how.

*Why bother? *
because every pipeline I've built, every dashboard I've spun up, and every analytics fix that slipped into production was really a small product meeting a real user's needs, even if no one slapped a logo on it. Until now, that work happened on gut feel rather than a roadmap; I've run user interviews and called it requirements gathering, dug through competitors and labeled it existing solutions, then picked the first item on my to-do list as the feature that made sense.
Now I want more, that's why I'm locking time on books, mentors, and cross-functional sprints so i can mesh deep tech skills with proven strategy and become the rare professional who designs bulletproof data stacks while steering product choices that move the needle.

*The untold shortfall
*
Far too many data tools stumble not because their code is broken, but because nobody ever bothered to learn what users really need or how the business plans to profit from it.

After spending years creating data products myself, I've witnessed the mess up close:

Brilliant code that sits idle because no-one knows how to use it

Dashboards that shine yet answer questions nobody asked

Helpful insights wrapped in clunky screens no-one wants to touch

Day-to-day I already diagnose and patch those gaps on instinct. Now I'd like to put a name to that skill and pair it with solid product strategy.

Plan: three projects, six months
While I push through these three projects I'll document every method I learn and map it back to the work so future teams can skip my mistakes.

Project 1: NBA Analytics Platform (Months 1-2)
I'll build a complete NBA stats portal, leaning on my Python/SQL/AWS stack but this time grounding the feature set in user interviews, market research and a clear business case.

Technical leverage: Python, SQL, AWS skills I already have
New product focus: Formal user research frameworks, market sizing, go-to-market strategy

Project 2: Real-Time Stock Market Analytics (Months 3-4)
Building on my experience with data pipelines, but adding formal product management processes for feature prioritization and user validation.

Technical growth: Kafka, Spark, real-time ML
Product formalization: OKRs, product roadmaps, stakeholder management

Project 3: AI-Powered Sports Predictions (Months 5-6)
This blends deep tech with full product strategy, a path I've touched before but never owned from start to finish with formal methods.

Technical mastery: MLOps, explainable AI, production deployment
Product expertise: Product-market fit validation, pricing strategy, growth metrics

What I'll Be Sharing

Every two weeks look for notes on:

Product frameworks in action: How AARRR, HEART, and Jobs-to-be-Done fit data work

Technical plus product choices: Architecture built for users, not only for engineers

Real user research: Moving from guesswork to proof

Business strategy: Shaping tech skills into hard-to-copy advantage

Career evolution: Documenting the product toolkit senior data engineers really use

My Learning Stack

Technical base: Python, SQL, AWS, PostgreSQL (solid already)

Technical growth: Kafka, Spark, XGBoost, MLflow, Kubernetes

Product playbook: Research methods, analytics tools, strategy, roadmaps

Why Formalize Now?

I keep doing product work but never label it. Every time I:

Interview stakeholders about their data needs -> user research

Choose which metrics to surface in dashboards -> feature prioritization

Design intuitive data interfaces -> user experience design

Justify infrastructure investments -> business case development

The aim isn't to start from zero, just to frame what I already do and layer on the big-picture tools that will make me sharper.

The Bigger Picture

Honestly, I'm finding myself naturally pulled toward a blend of roles: Technical Product Manager, Data Product Manager, or maybe a Senior Data Engineer who carries product ownership. Each one leans on the code I've written but also welcomes the gut feel for customers that I've been growing.

More than titles, I'm after the chance to build data products that don't just run in the background: they get picked up, deliver real value, and solve everyday headaches in a simple way.

Lets Connect!
I'm genuinely excited to bring you along on this road. If you're:

a data engineer who somehow ended up doing product work
someone who actually crossed over to formal product roles
building data tools and has lessons to trade
curious about the sweet spot where deep tech meets big-picture strategy

drop a note below! How did you experience the jump from code to product?

You can also follow me on GitHub, where I will be posting code, sketches, and plain-language docs.

Cheers to making all those unlabeled product skills official!

Next post: Reality check: git habits, python tricks, LeetCode drills, and a no-nonsense peek at what starting from scratch feels like when you're juggling a day job.

DEV Community: timi-lanre

[Data to Product]

Turning Six Months of On-the-Job Learning into a Structured Product-Minded Playbook

Turning Six Months of On-the-Job Learning into a Structured Product-Minded Playbook