Data Analyst or Data Engineer or Analytics Engineer or BI Engineer ?

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Apr 30, 2025 - 02:38

If you’ve followed me for a while, you probably know I started my career as a QA engineer before transitioning into the world of data analytics. I didn’t go to school for it, didn’t have a mentor, and didn’t land in a formal training program. Everything I know today—from SQL to modeling to storytelling with data—is self-taught. And believe me, it’s been a journey of trial, error, learning, and re-learning.

The Dilemma That Changed My Career

A few years ago, I started thinking about switching organizations. Like many people in fast-evolving tech roles, I faced a surprisingly difficult question:

What role am I actually doing? Which roles should I apply for?

On paper, I was a Data Analyst. But in reality, my role straddled several functions: writing SQL pipelines, building dashboards, defining KPIs, and digging into product analytics. I wasn’t sure whether I should be applying for Analyst roles, BI roles, or something entirely different.

To make things worse, back then, job titles were vague, and job descriptions were bloated with buzzwords. You’d find a posting titled “Data Analyst” that listed requirements like:

Build ML pipelines
Write complex ETL scripts
Maintain data lakes
Create dashboards
Present executive-level insights
And oh, by the way, be great at stakeholder management

It was overwhelming and confusing. And I know I’m not alone in this.

Fast forward to today: thankfully, things are evolving. There’s still overlap between roles, but organizations have started to define them more clearly. In this article, I want to break down the real differences between data roles, through the lens of a real-world example.

A Real-World Scenario: Meet Quikee

Let’s imagine a fictional quick-commerce startup called Quikee, launching across multiple Indian cities. Their value proposition? Deliver groceries and essentials within 10 minutes.

Customers place orders through the app or website. Behind the scenes, there are micro-warehouses (also called “dark stores”) across cities, and a fleet of delivery partners who make those lightning-fast deliveries.

Now, let’s walk through the data needs of this company—from the moment an order is placed, to the dashboards executives use in their Monday morning meetings.

Step 1: Capturing and Storing Raw Data

The moment a customer places an order, transactional data is generated:

Timestamps
Order ID
Items ordered
Price
Discount codes
Customer location
Payment method
Assigned delivery partner

Let’s assume Quikee uses Amazon Kinesis to stream this data in real time to an S3 data lake. That stream is high-volume, time-sensitive, and crucial for business tracking.

But here’s the catch: raw data is messy. You can’t use it directly for decision-making.

So what happens next?

Step 2: Building Data Pipelines

Enter the Data Engineers.

They are responsible for:

Ingesting real-time data
Validating schema consistency
Handling failures and retries
Writing pipelines to move data from S3 into a data warehouse (say, Snowflake or Redshift)

This is where ETL (Extract, Transform, Load) or ELT pipelines come into play. Data engineers clean, format, and structure the data to make it queryable.

For example, an order table might get split into: