You will get Custom Data Pipeline & Automated BI Dashboard Setup
Project details
Most data pipelines are built once, then quietly break the first time your data structure changes. I build them to hold up, with clean architecture, proper error handling, and automation that doesn't need babysitting. With 17+ years across full-stack development and data engineering, I don't just move data from A to B, I understand what it's being used for, so the pipeline and dashboard are shaped around real decisions, not generic templates. You'll get a system that updates itself, a dashboard that answers the actual questions you're asking, and documentation clear enough that your team isn't dependent on me to keep it running. I'd rather build one pipeline that works for two years than five that need constant fixing.
Database Type
MySQL, MS SQL, SQLite, PostgreSQL, MongoDB, Azure Cosmos DBWhat's included
| Service Tiers |
Starter
$450
|
Standard
$950
|
Advanced
$1,240
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 15 days |
Number of Revisions | 1 | 2 | 4 |
Number of Tables Added | 3 | 8 | 15 |
Schema Diagram | - | ||
Permissions Setup | - | ||
Import/Export Data | |||
Admin Panel Setup |
Frequently asked questions
About Hakeem
Full-Stack Developer | AI Agent Systems | Data & Ads Specialist
Agadir, Morocco - 3:57 am local time
The Foundation, Full-Stack Development
It started with building things that work, and work well. Over the years I've built fast, secure, scalable web and mobile applications using Next.js, Remix, React, Node.js, TypeScript, Tailwind CSS, Laravel, PHP, Go, and C#, backed by MongoDB, PostgreSQL, and Firebase. On the infrastructure side, that grew into real fluency with AWS, Google Cloud, Docker, Kubernetes, and CI/CD pipelines, the unglamorous plumbing that decides whether a product survives real traffic or just a demo. Good software should feel like a well-oiled door hinge, you only notice it when it's missing.
The Expansion, Data Engineering & Analytics
Building products naturally led to asking what they were actually telling me. That curiosity turned into real depth in SQL, Python, and Scala, designing data pipelines and business intelligence systems that turn raw, messy data into decisions, through customer segmentation, predictive analysis, and reporting that holds up under scrutiny, not dashboards that merely look good in a meeting. Raw data is just noise until someone gives it a shape.
The Sharpening, Advertising & Growth Marketing
Understanding data well enough to build with it eventually meant understanding it well enough to spend money on it responsibly. As a Google & Meta certified advertising specialist, with Microsoft Partner experience alongside it, I've managed over $175,000 in ad spend, grounded in precise tracking, real ROI measurement, and research-based targeting matched to the right ad type and audience from day one, not adjusted after the budget's already gone. That's paired with SEO, branding, and conversion-focused UI/UX, so results keep compounding long after any single campaign ends. Running ads without tracking is like fishing without knowing what's under the water, you might catch something, but you'll never know why.
The Frontier, AI Memory Systems & Agent Development
Most recently, that same instinct, build it well, understand it deeply, then make it useful, has pulled me into AI memory architecture and agent development. I work on giving AI agents persistent, structured memory, systems that let an agent recall context, learn from past interactions, and act with continuity instead of starting from zero every session. It's less about making an agent sound smart in a single reply, and more about giving it a real thread to hold onto across time, the difference between an assistant that reacts and one that actually remembers.
What Ties It Together
Each stage didn't replace the one before it, it sharpened it. The developer instincts inform how I structure data. The data instincts inform how I target ad spend. The marketing instincts inform how I think about what an AI agent should remember and why. I'd rather do fewer things exceptionally well than pad a portfolio with everything I've ever touched, and I bring the same care to a data pipeline, a full application, an ad campaign, or an agent's memory layer that I'd want if it were my own business on the line.
Certifications & Trust
Google & Meta certified advertising specialist, Microsoft Partner experience, verified expertise rather than a self-declared claim on a profile page.
Skills Snapshot
- Full-Stack Development: Next.js, Remix, React, Node.js, TypeScript, Laravel, PHP, Go, C#
- Databases: MongoDB, PostgreSQL, Firebase
- Cloud & DevOps: AWS, Google Cloud, Docker, Kubernetes, CI/CD
- Data Engineering: SQL, Python, Scala, data pipelines, business intelligence
- Marketing & Growth: Google Ads, Meta Ads, SEO, market research, branding, conversion-focused UI/UX, ROI tracking
- Emerging Focus: AI memory systems, agent development, persistent context architecture for autonomous agents
Steps for completing your project
After purchasing the project, send requirements so Hakeem can start the project.
Delivery time starts when Hakeem receives requirements from you.
Hakeem works on your project following the steps below.
Revisions may occur after the delivery date.
1. Discovery & Requirements Call
Kickoff call or written brief to confirm your data sources, key metrics, and the business goals the pipeline needs to support.
2. Data Source Audit & Architecture Planning
Review your existing data sources and map out the pipeline architecture, deciding how data will flow, transform, and land before any code is written.