Senior Analytics Engineer
MetricMend
An end-to-end Analytics Engineering case study combining modern data engineering, semantic modeling, and AI-driven decision support..
Every analytics request deserves more than a dashboard. It deserves context, trusted metrics, and confidence.
Analytics Engineering Lifecycle
Business Discovery
Requirements Engineering
Source System Assessment
Lakehouse Architecture
Data Engineering
Dimensional Modeling
Semantic Layer
AI Decision Intelligence
Executive Experience
Engineering Observation
Why I Built
MetricMend
Throughout my career, I worked on modern analytics platforms, building data models, semantic layers, dashboards, and reporting solutions for business teams. While analytics technology continued to evolve, I noticed a recurring challenge: business users still relied heavily on analytics teams to interpret metrics, investigate trends, and answer follow-up questions before they could make confident decisions.
Around the same time, AI-powered analytics platforms began reshaping how users interacted with data. Rather than viewing those products as competitors, I saw them as validation that the industry was moving in an exciting direction. I wanted to explore the same problem from my own Analytics Engineering perspective and validate my own architectural ideas from end to end.
MetricMend became that exploration. I designed the project as a complete Analytics Engineering case study—starting with business discovery, requirements engineering, source system assessment, lakehouse architecture, data engineering, dimensional modeling, semantic governance, and finally an AI-driven decision experience. The application itself is simply the interface that brings those engineering decisions together.
Business Challenge
Modern dashboards answer
questions.
They rarely guide decisions.
The Problem
Traditional BI platforms answer questions only after dashboards are built. Business users still depend on analysts to interpret data, define metrics, and explore follow-up questions.
Design Objective
Design an Analytics Engineering platform that unifies trusted data, governed metrics, and AI-assisted decision support into a single analytical experience.
Discovery
Every analytics platform
begins with understanding
the business.
01
Business Goals
Understand the decisions stakeholders need to make before defining KPIs or reports.
02
Stakeholder Interviews
Capture expectations, reporting pain points, ownership, and success criteria.
03
Requirements
Translate business conversations into measurable analytics requirements and governed metrics.
04
Source Assessment
Identify available systems, data quality, relationships, and integration opportunities.
Source Systems
Trusted analytics
begins with
trusted data.
Source
CRM
Customer interactions, accounts, opportunities and sales activity.
Source
ERP
Orders, invoices, inventory, procurement and financial operations.
Source
Marketing
Campaign performance, attribution, traffic and customer acquisition.
Source
Finance
Revenue, expenses, profitability and executive financial reporting.
Source
Support
Customer tickets, SLAs, satisfaction and operational performance.
Source
Operations
Business processes, fulfillment and operational KPIs.
Solution Architecture
Turning business
questions into
trusted decisions.
Architecture Layers
01 Sources
02 Lakehouse
03 Modeling
04 Governance
05 Intelligence
06 Experience
Analytics Engineering
Engineering a scalable lakehouse using the Medallion Architecture.
Raw Data Ingestion
Ingest raw data from business systems while preserving lineage, history, and auditability.
Transformation & Quality
Standardizing schemas, validating business rules, cleansing records and enriching datasets for analytics.
Analytics Ready
Deliver analytics-ready datasets optimized for dimensional modeling, semantic models, reporting, and AI.
Data Modeling
Transforming raw data
into business-ready
analytical models.
01
Business Events
Identify measurable business processes and translate them into analytics-ready fact tables.
02
Fact Tables
Capture transactional events, measures and business activity that power reporting and AI insights.
03
Dimension Tables
Design reusable dimensions including Customer, Product, Date, Geography and Business Units.
04
Star Schema
Build performant analytical models with clear relationships that simplify reporting and improve query performance.
Semantic Layer
One business language
across reporting,
analytics, and AI.
Governed Metrics
01
Consistent Definitions
Business metrics are defined once and reused across dashboards, reports, and AI-generated answers.
02
Reusable Logic
Time intelligence, calculations, and business rules are centralized instead of recreated in every report.
03
Governed Access
Metric ownership, approved dimensions, and calculation logic create a trusted analytical contract.
Decision Intelligence
AI should understand
your business,
not just your prompt.
Business Question
Intent Detection
Context Planning
Semantic Validation
SQL Generation
Execution
Visualization
Decision Support
Executive Experience
From business
question
to confident decision.
01
Ask
Business users ask questions naturally without needing dashboards, SQL, or technical knowledge.
02
Understand
Questions are interpreted using governed metrics, business definitions, and organizational context.
03
Analyze
Relevant datasets are retrieved, validated, and transformed into meaningful analytical insights.
04
Explain
AI summarizes findings, highlights trends, surfaces anomalies, and recommends where to investigate next.
05
Decide
Executives receive trusted answers that improve confidence in every business decision.
Technology Decisions
Every technology
solved an engineering
challenge.
Databricks
Designed as the analytics foundation to support scalable ETL, Medallion Architecture, and analytics-ready datasets.
Delta Lake
Selected to provide reliable storage, versioning, ACID transactions, and trustworthy analytical data.
FastAPI
Implemented as the orchestration layer connecting AI workflows, business logic, and semantic analytics.
Next.js
Built a fast, responsive executive experience focused on decision making rather than dashboard navigation.
Supabase
Managed authentication, user access, persistence, and multi-workspace security.
OpenAI
Enabled natural language understanding while grounding every response in governed business metrics.
Business Outcomes
Engineering that
enables better
business decisions.
Trusted Business Metrics
A single governed source of truth for KPIs, calculations, and executive reporting.
Reusable Analytics Foundation
Semantic models, dimensional design, and business logic created once and reused everywhere.
AI Grounded in Business Context
Every AI response is backed by governed metrics instead of isolated prompts or raw data.
Executive Self-Service
Leaders can explore questions, investigate trends, and make decisions without relying on static dashboards.
Scalable Architecture
A modular foundation designed to evolve as business requirements, data sources, and AI capabilities grow.