for Dr Elahi

Part 2:

Oliver’s business is now growing. He built a company and has sales agents and sales supervisors working for him all over England. He realizes using google spreadsheets is not sustainable.

He wants to invest in a scalable system and he is thinking to recruit a software engineer and a data engineer (you).

The software engineer has to come up with a mobile app that allows the sales agents to report their sales from the app.

The app should be connected with a data pipeline that would eventually make the data ready for analysis.

– What would be your strategy to build a strong and reliable data pipeline and how will you structure it ? Explain the different steps for processing the data, what makes each step important?

Mention which tools would you use for each of these steps. Feel free to argument as much as you feel it relevant You’re free to make assumptions about the mobile app e.g. The raw data is stored in a No-Sql database…

Part 3:

Each London major city is split between different Sales Supervisors, each one in charge of multiple Sales Agents.

The Sales Agents are the ones who actually make the sales. They get assigned a sales target, and their performance is measured as % of sales out of their target. The Supervisors’ performance is simply the aggregation of their Sales Agents performance.

1.1 Build a script that reads the csvs attached below, and returns the two best performing Supervisors for each city. Please note that you may have to scrape among redundant or incomplete data.

CSVs : https://www.dropbox.com/sh/5zcnthswnpt1j7z/AAC9AH0IDyvod6PeQlrJzpkHa?dl=0

1.2 Assuming another data engineer was responsible to build the data pipeline as suggested in PART 2, and knowing the csv files was exported from a postgres database that stores the data ready for analysis, how would you improve that pipeline to get a better data quality?

Explanation & Answer

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