As a Data Engineer at Reedsy, you will be responsible for managing our data platform, including the design of a pipeline to extract data from various sources through various processes, apply appropriate transformations, and output results into visualization tools and decision support systems.
We are looking for data-aware candidates that can help us with creating novel ways to approach out data, both from the engineering and analytical perspectives. For this reason, we will assess and value your data analytics skills in addition to those required for classic Data Engineering roles.
Describe your thought process with detail and clarity. Even if you don't get to a solution/conclusion on any of the following questions, make sure that you provide a detailed explanation to help us understand your approach and assumptions.
Write a paragraph about yourself, your hobbies and your major achievements in your career or study so far. Add another one about your professional experience and commercial projects you've been involved with.
Consider Bookly, an internet company that owns a marketplace for selling books, a blog and other complementary online products with an overlapping user base. Each product is backed by its own independent Ruby web server and relational SQL database.
Bookly has approx 100k books and 1M users.
The Marketing Team at Bookly wants to merge and analyze all the data that is being collected by the different products in order to extract useful business insights of various kinds. Examples of such analytics include (but are not restricted to):
- List all-time top rated books and trending ones;
- Measure user sign-up rate over certain periods (weekly, quarterly, etc);
- Show the total number of real-time (current) page views for any given book description page (product page);
- ...
Design a conceptual data pipeline to drive and aggregate data from all the different sources to ultimately be accessible by a user-friendly data exploration/dashboarding tool of your choice. Feel free to pick any technology available (e.g. open source, cloud providers, etc.).
Describe the different components of the architecture, tools involved and compare possible approaches.
The Customer Support team spends considerable time scanning through customer reviews and comments in order to filter out illegitimate ones. Multiple factors can contribute to label comments as authentic or not:
- Comment is made by a registered user vs. anonymous;
- Level of user activity (eg: number of past reviews and comments);
- Content of the comment (eg: unauthorized advertising)
- ...
In order to automate the filtering process, design a conceptual, real-time, decision support system that takes data as input (user properties, user actions, content, etc) and automatically labels comments as legitimate/illegitimate for the Customer Support Team to quickly flag and remove the unwanted.
Describe the different components of the architecture, tools involved and compare possible approaches.
The marketing Team at Bookly introduced A/B Testing on their blog - each blog post will show a given registration popup from a set of pre-configured popups. The raw dataset in dataset.tsv offers a table with various content properties and the conversion rates for each combination of blog post and registration popup.
What is the major contributor to user registrations? In other words, what is the most relevant factor that contributes the most to convert user views into user registrations?
Describe in detail all the steps you take to perform the analysis, provide code snippets, relevant data transformations and results.
Explore the dataset. What other insights can be extracted?
Describe in detail all the steps you take to perform the analysis, provide code snippets, relevant data transformations and results.