Kavya's Jobaaj Learnings Review: From Operations to Marketing Analyst at Deloitte

"The first round was with the manager — a mix of technical and managerial questions. They focused on Excel skills, data analysis basics like normalization, central tendency, and handling unclean data. The weekend classes at Jobaaj really helped me there." — Kavya

Kavya holds an MBA in marketing from a college in Visakhapatnam. After graduating she spent about three years in operations and was not happy there. She took a break from work to join Jobaaj Learnings' Data and Business Analytics Program, then applied for analyst roles — and cleared a five-round process to become a Marketing Analyst at Deloitte, where she had been for three months when this was recorded.

If you are searching “is Jobaaj Learnings genuine”, “is Jobaaj real or fake” or “how hard is a Deloitte analyst interview”, this is the most detailed interview breakdown in the series — five rounds, described one by one, including a timed one-hour Excel assessment. It is also honest about the cost: she left her job to retrain. No salary is disclosed. Read it alongside the other conversations in our Interview Series and the wider feedback on the reviews page.

Who Is Kavya? MBA, Then Three Years She Didn't Want

Kavya completed an MBA in marketing at a college in Visakhapatnam, and then went into operations, where she spent about three years.

Her account of why she left is short and unembellished:

"I did my MBA and then started working in operations. But I wasn't very happy in that field. I wanted to move into data analytics. So I took a break, joined the Jobaaj program, and then applied for analyst roles."

The Deloitte Interview: Five Rounds, Broken Down

This is the most useful section on the page. Kavya describes each stage in order:

What the Excel Test Actually Tested

Kavya is specific about this, and it corrects a common assumption:

"They gave practical tasks and one hour to complete them on a virtual call. It wasn't about building dashboards, but more about cleaning data, arithmetic formulas, and pivot tables."

So: no dashboards. The test was data cleaning, formulas and pivot tables, done live and against the clock. If you are preparing for an analyst assessment, that is where the practice should go — speed and accuracy on unglamorous Excel work, not visual polish.

Two Rounds That Weren't Technical

Worth registering: of five rounds, two were about communication and behaviour — a dedicated communication round, and a behavioural round facing three managers simultaneously. Technical skill got her through the first two stages; it would not have got her through the last three.

What She Actually Does at Deloitte

Three months in, Kavya describes the work concretely:

"I work in the data reporting and automation team that supports Deloitte's Australian marketing team. They request reports and dashboards, and we deliver them."

Her tools:

• Tableau — dashboards
• Excel — smaller datasets
• VBA — automation of repetitive reporting tasks

Note what is not in that list: Python. She recommends learning it, but it is not yet part of her daily work — a distinction worth keeping straight when you plan your own learning.

Her Skill Roadmap for Aspiring Analysts

Asked what to learn, Kavya gives a clear ordering rather than a list:

1. Excel first. “Excel is the foundation. You should be strong in that first.” Her own interview confirms it — an entire round was a timed Excel test.
2. Then one dashboarding tool — Power BI or Tableau. Her reasoning: “Once you know one well, learning the other becomes easy.” Depth in one beats shallow familiarity with both.
3. Then Python. “In our firm, they even say Python is the new Excel.”

Marketing Analyst vs Other Analyst Roles

Kavya addresses a question that confuses a lot of people choosing a specialism:

"In marketing, we don't handle transaction data like sales, but we work with client databases. The data types might change, but the process of analysis is more or less the same."

The practical implication: the domain label on an analyst role — marketing, HR, finance — changes what data you touch, not the skills you need. Which means analyst skills transfer across domains more readily than job titles suggest.

Pros and Cons, Based on Kavya's Account

What worked:

• Weekend classes she credits directly for clearing the technical round — specifically normalisation, central tendency and unclean data
• Excel training deep enough to pass a timed, practical, one-hour assessment
• A career switch from operations into analytics at a major firm
• A role using Tableau, Excel and VBA on live reporting work

What her account shows honestly:

• She had to stop working to do it — a career break with no income.
• Five rounds, including two non-technical stages and a panel of three managers.
• She applied for the roles herself after the programme, on her own description.
• No salary is disclosed and no rating was asked for or given.
• She had three years of prior work experience and an MBA — not a fresher's profile.

Marketing Analyst Interview Questions and Model Answers

Why did you switch from operations to analytics?

Model answer: In operations I found the parts of the job I enjoyed were all data work — it was logical, and the output actually changed decisions. I took a deliberate break to build the analytics skills properly rather than trying to drift sideways into it, and moved into a role where analysis is the job rather than a side effect of it.

What is data normalisation and why does it matter?

Model answer: Organising data to reduce redundancy and improve consistency — breaking large tables into smaller related ones linked by keys. It matters because duplicated data drifts out of sync, and a database where the same fact is stored in three places will eventually disagree with itself.

How do you clean messy data in Excel?

Model answer: Remove Duplicates, TRIM and CLEAN for the basics, filters to isolate the problem rows, and IFERROR and PROPER for formatting and error handling. Conditional formatting is the fastest way to spot anomalies visually, and a manual spot check at the end catches what the formulas did not.

What is central tendency and how is it used?

Model answer: Mean, median and mode — the measures that describe the centre of a dataset. They summarise a general trend, but which one you use matters: an average click-through rate is misleading if a handful of campaigns are outliers, which is when the median tells the truer story.

Which Excel functions do you use in reporting?

Model answer: VLOOKUP and INDEX-MATCH, IF, SUMIFS and COUNTIFS with logical operators, and pivot tables for summarising. Data validation and conditional formatting go into anything a colleague will use, so the report stays readable and hard to break.

What is VBA and how have you used it?

Model answer: Visual Basic for Applications — the automation layer inside Excel. I use it for repetitive reporting: generating reports, applying formatting, running macros for processes that repeat weekly. It saves time, but the bigger gain is removing the human error that comes from doing the same manual steps every cycle.

How would you build a dashboard for a marketing team?

Model answer: Start by asking which metrics they actually make decisions on — campaign performance, engagement, ROI — rather than showing everything available. Clean and structure the data first, then build in Tableau with filters so the team can interrogate it themselves instead of requesting a new version each time.

What challenges come with real datasets?

Model answer: Missing or inconsistent data arriving from multiple sources. The fix is thorough cleaning, documenting what you changed and why, and going back to the data owner when something cannot be resolved from the data alone — guessing is how a reporting error becomes permanent.

Kavya's Advice for Aspiring Analysts

• Master Excel before anything else. An entire Deloitte round was a timed Excel test.
• Practise cleaning and pivot tables, not dashboards, for assessments — that is what hers actually contained.
• Learn one dashboarding tool properly. Power BI or Tableau; the second one comes easily afterwards.
• Add Python next. Even if it is not in your daily work yet, it is where the expectation is heading.
• Prepare for the non-technical rounds. Two of her five stages were communication and behaviour, one of them facing three managers.
• Don't over-index on the domain. Marketing, HR or finance analytics use the same process on different data.

Full Podcast Transcript

The complete conversation, unedited.

Frequently Asked Questions

Is Jobaaj Learnings genuine, or is it fake?

This page documents one recorded case: Kavya, an MBA graduate with three years in operations, who took a career break to complete the Data and Business Analytics Program and cleared a five-round process to become a Marketing Analyst at Deloitte. Employer, role, team and interview stages are all named on camera. No salary is disclosed. See the wider review set on the reviews page.

How many rounds is a Deloitte analyst interview?

Five, in Kavya's case: a manager round mixing technical and managerial questions, a timed Excel assessment, a communication round, a behavioural round with three managers, and a final conversation with the VP of the marketing team.

What does the Excel assessment involve?

Practical tasks completed in one hour on a live virtual call — data cleaning, arithmetic formulas and pivot tables. Explicitly not dashboard building.

Which skills should an aspiring analyst learn first?

Kavya's order: Excel to a strong standard, then one dashboarding tool (Power BI or Tableau) learned properly, then Python — which her firm describes as the new Excel.

What does a Marketing Analyst actually do?

In her case, working in a data reporting and automation team supporting Deloitte's Australian marketing team — building the reports and dashboards they request, using Tableau, Excel and VBA.

Is marketing analytics different from other analyst roles?

The data differs; the process does not. Marketing works with client databases rather than sales transaction data, but the analysis approach is largely the same — which means the skills transfer between analyst domains.