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Data Engineer Interview Questions

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Data Engineer Interview Questions

Data engineers are IT professionals who are needed in almost every industry. Data engineers monitor data trends to determine best next steps for companies. A critical part of a data engineer job is to process raw data into usable data by creating data pipelines and building data systems.

Top Data Engineer Interview Questions & How To Answer

Question 1

Question #1: Can you describe in detail your level of expertise with programming languages?

How to answer
How to answer: Before the interview, review your resume and/or portfolio and make a list of the programs you are most proficient with. If you find that you are lacking the expertise in a program that the company predominately uses, describe yourself as a highly motivated self-starter who will work tirelessly to learn the program(s).
Question 2

Question #2: Explain data engineering in your own words.

How to answer
How to answer: Highlight your role in relation to the larger organization and other roles like data scientists to clearly define your contribution to the overall system of business. Clarify the difference between a database-centric engineer and a pipeline-centric engineer.
Question 3

Question #3: Can you describe your experience working with Apache Hadoop and cloud data management environments?

How to answer
How to answer: Research the company's software, data cloud products, and use of Apache Hadoop to be prepared for this inquiry. Data Engineers must be fluent in programming languages and data management systems used throughout the industry such as Apache Hadoop.

20,202 data engineer interview questions shared by candidates

Build a web application that allows users to learn who represents them in the US House of Representatives. User Flow 1. User enters their zip code in validated form field. 2. User clicks submit button, or hits Enter key when input is focused. 3. User is returned a summary of who their representative is, including links to learn more. Resources: The `/data` folder in this repo contains two datasets: `legislators.json` lists current representatives associated with the states and district numbers they've served in, and `zipcodes-districts.json` lists every US zip code with its associated state and district number.
avatar

Web Engineer With Data Visualization Focus

Interviewed at Swayable

4.8★
Feb 10, 2022

Build a web application that allows users to learn who represents them in the US House of Representatives. User Flow 1. User enters their zip code in validated form field. 2. User clicks submit button, or hits Enter key when input is focused. 3. User is returned a summary of who their representative is, including links to learn more. Resources: The `/data` folder in this repo contains two datasets: `legislators.json` lists current representatives associated with the states and district numbers they've served in, and `zipcodes-districts.json` lists every US zip code with its associated state and district number.

Spark optimizations: what are the optimizations that can be done for the below snippet code: shoppers_df (customers description DF) 250MB, 15M records: schema: StructType = StructType(Array(StructFiled("shopper_id", LongType, nullable = True), StructField("retailer_id", StringType, nullable = True), StructField("shopper_group_id", StringType, nullable = True), StructField("join_date", DateType, nullable = True), StructField("shopper_type", StringType, nullable = True), StructField("gender", StringType, nullable = True))) sku_df (dimension DF): 15 MB, 90K records purchase_df (transactions DF): 50GB of parquet compressed files 5,000,000,000 records. schema: StructType = StructType(Array(StructFiled("shopper_id", LongType, nullable = True), StructField("product_id", LongType, nullable = True), StructField("pos_id", IntegerType, nullable = True), StructField("purchase_date", DateType, nullable = True), StructField("units", DoubleType, nullable = True), StructField("total_spent", DoubleType, nullable = True))) Current code: products_purchased_df = purchase_df.alias("purchase").join(shoppers_df, on = "shopper_id", how = "left outer").join(sku_df.alias("sku"), on = "product_id").select(Col("purchase.*"), Col("sku.*")) usage: status_df = products_purchased_df.groupBy(["shopper_id", "product_id"]).agg(...) Optimize join statement
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Data Engineer

Interviewed at ciValue

4.2★
Mar 16, 2023

Spark optimizations: what are the optimizations that can be done for the below snippet code: shoppers_df (customers description DF) 250MB, 15M records: schema: StructType = StructType(Array(StructFiled("shopper_id", LongType, nullable = True), StructField("retailer_id", StringType, nullable = True), StructField("shopper_group_id", StringType, nullable = True), StructField("join_date", DateType, nullable = True), StructField("shopper_type", StringType, nullable = True), StructField("gender", StringType, nullable = True))) sku_df (dimension DF): 15 MB, 90K records purchase_df (transactions DF): 50GB of parquet compressed files 5,000,000,000 records. schema: StructType = StructType(Array(StructFiled("shopper_id", LongType, nullable = True), StructField("product_id", LongType, nullable = True), StructField("pos_id", IntegerType, nullable = True), StructField("purchase_date", DateType, nullable = True), StructField("units", DoubleType, nullable = True), StructField("total_spent", DoubleType, nullable = True))) Current code: products_purchased_df = purchase_df.alias("purchase").join(shoppers_df, on = "shopper_id", how = "left outer").join(sku_df.alias("sku"), on = "product_id").select(Col("purchase.*"), Col("sku.*")) usage: status_df = products_purchased_df.groupBy(["shopper_id", "product_id"]).agg(...) Optimize join statement

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