Interviewing ETL Developer in Big Data

The larger a company becomes, the more frequently it needs to process and transmit large amounts of data. This can be due to the obsolescence of Database Management Systems (DBMS) and changes in database requirements and architecture. ETL developers are responsible for collecting, processing, and storing this information. In this article, you will learn about the role of ETL developers in big data, their essential skills, the structure of interviews for this position, and some suggested interview questions. These insights will help you hire the best ETL Developer for your company.

What does an ETL developer in big data do?

Understanding the functions assigned to ETL programmers is straightforward when you break down the abbreviation ETL:

Extract: Information is extracted from various sources, including API interfaces, cloud services, other websites, CRM systems, SQL tables, NoSQL objects, MS Excel documents, and text files (e.g., TXT, DOCX). This step also involves storing the extracted data in an intermediate database, where it is checked for accuracy and integrity.
Transform: This stage involves analyzing, filtering, and comparing the extracted data. Since the data can come in different formats, such as CSV, XML, JSON, text, objects, and arrays, programmers often need to convert it into a uniform format. Additionally, they must clean up duplicates, remove spam, and standardize the stored data.
Load: The final stage involves storing the processed information on a server. Given the large volumes of data, it’s crucial for developers to properly distribute the load to ensure efficiency and avoid system overloads.

What an ETL developer should know?

All skills required for an ETL developer can be divided into hard and soft skills. The first includes technical knowledge and skills; the second includes personal qualities and communication abilities. Both groups are equally important. There is no specific programming language requirement. ETL developers have any language (C#, C++, Java, PHP, Python), but in most cases, they will need to use the language used by the company. So if you’re looking for a candidate for an interview, you may need to know if is there knowledge language familiar with your company.

Relational and non-relational databases.
SQL (for relational databases) and NoSQL (for non-relational databases) languages.
DBMS: PostgreSQL, MySQL, Microsoft SQL Server (for SQL), MongoDB, and Redis (for NoSQL)—you can choose one per DB type.
Configuring databases according to the type of multidimensional cubes and tools for it: MDX, SSAS, and OLAP.
Spatial modeling.
Data integration platforms include IBM InfoSphere, OpenText, Informatica PowerCenter, and Pervasive Data Integrator.
Cloud data storage: Cloud Big Data, Yandex.Cloud, and Google Cloud.
Modeling programs: Embarcadero and Toad Data Modeler.
SSIS data migration platform.
Data processing, analysis, and visualization tools: Metabase, SAP BusinessObjects, Airflow, and Apache Spark Streaming.
Hadoop ecosystem components: HDFS, Spark, HBase, Hive, and Sqoop experience.
Sybase product suite: DBMS and software for management and information analysis.

What ETL developers need to know besides this?

This is by no means an exhaustive list; Oracle GoldenGate, Docker, Kafka, and other programs and platforms could also be included. Additionally, there is an excellent list of soft skills requirements. For example, willingness to work in a team, good communication skills, ability to accept criticism, tolerance for stress, ability to learn, and self-motivation. ETL developers must have strong English skills. This requirement applies anywhere in programming, but it is particularly important here.

Interview structure for ETL developer

You can structure your ETL developer interview into three rounds:

Round 1: Technical Screening (45 minutes)
Evaluate the candidate’s ETL and big data knowledge, as well as programming skills.
Round 2: In-Depth Technical Interview (1 hour)
Assess the candidate’s ability to design and optimize ETL workflows in a big data environment.
Round 3: Hands-On Coding Test (1.5 hours)
Examine the candidate’s coding skills in a real-world scenario.

Interview questions for ETL developer

Explain the stages of the ETL process (Extract, Transform, Load).
What is the role of a large data developer in an ETL environment?
How do relational databases differ from non-relational ones?
How would you handle the extraction of data from various sources such as APIs, databases, or files?
How do you manage data inconsistencies during extraction?
Tell me about your experience with SQOOP or KAFKA.
What are the most popular methods for tasks that include filtration, aggregation, and merging?
Describe some standard file formats (such as CSV and JSON) and the methods for converting between them.
If you had missing values in your dataset, how would you handle duplicates during transformation?
Can you explain how error handling works when using partition tables during the loading process?
What techniques can be used to improve the Hadoop cluster’s performance in data-loading processes?
Design an ETL workflow for data integration given a business scenario.
What factors determine your choice of extraction, transformation, and loading tools?
What measures have you taken to make your ETL patterns scalable and fault-tolerant?
Share your background working with distributed file systems such as HDFS.
How is Apache Spark utilized in ETL processing for big data?
How do you leverage cloud platforms like AWS and Azure in managing ETL workflows?
Describe your familiarity with SQL and NoSQL databases.
Explain how data modeling applies to relational and non-relational databases.
What steps would you take to increase the speed of an ETL workload on a database?
Tell me about your experiences with data integration platforms like Informatica PowerCenter or IBM InfoSphere.
How do these platforms facilitate ETL development and deployment?
How can scheduling and job orchestration functionalities in data integration tools enhance the overall process?
Given a real-world data processing scenario, write code (e.g., Python, Java) to demonstrate your problem-solving skills through coding.