Although the skills needed to succeed within a data engineering role can vary, there are a few common elements you can expect to come up with in both job interviews and the day-to-day workload.

Coding & Algorithm Prerequisites

The three main topics evaluated in data engineering interviews are Python, SQL, and algorithms & data structures.

Experience with SQL is a core requirement because it’s the main tool for retrieving data. Learning how to optimize queries and adjust them to requirements can be challenging.

For data extraction and processing, you will need to be very familiar with a scripting language. Python is highly recommended, with a particular focus on the data processing library Pandas. If you work on a smaller team and need to perform analytics, experience with Matplotlib and NumPy will be helpful for plotting data and numeric processing.

Data structures and algorithms are also important to understand for optimizing efficiency within database systems.

This means having an understanding of how to design algorithms over different data structures (arrays, linked lists, queues, stacks, graphs, etc.), such as merge sort or Dijkstra’s algorithm.

It especially includes knowing how to classify an algorithm’s efficiency using the Big O Notation (both in terms of time and memory). You’ll need this to be able to make efficient design decisions in the future.

Data Engineering Concepts

The data engineering-specific topics you will need for designing systems architecture include:

• ETL (extract, transform, load)
• ELT (extract, load, transform)
• OLAP (online analytical processing)
• OLTP (online transactional processing)

Additionally, you’ll need to be familiar with how database management systems (DBMSs) work overall and have in-depth experience working with at least one specific type. This includes both SQL DBMSs (such as MySQL, Postgres, SQLite, or Oracle) and NoSQL DBMSs (Redis, Cassandra, or MongoDB).

Experience working with different data lakes, data processors, orchestration tools, and streaming systems is vital. Some of the tools that you should familiarize yourself with include:

• The AWS ecosystem, including S3, Redshift, or EC2 (data lakes)
• Apache Airflow, Dagster, and Argo (orchestration tools for automating data flow)
• Apache Spark, Storm, and Hadoop (for data processing)
• Amazon Kinesis and Kafka (streaming systems for data influxes)

Finally, you’ll need to know how to carry out a database design, which includes clarifying end-user requirements for schema design, choosing a DBMS solution and a tech stack, and providing solutions to possible edge cases.