A key skill for a data engineer is proficiency in programming languages used in data processing and analysis, such as Python and Scala, as well as knowledge of SQL for database management and manipulation. It is important that the candidate demonstrates experience in using these languages to create effective data pipelines, process datasets and integrate with different data sources.

In a data engineer's job, experience with the Hadoop ecosystem, including HDFS, MapReduce, Hive, and Spark, is often crucial. The ability to work with these tools enables efficient processing and analysis of large volumes of data, which is critical for organisations that base their decisions on data.

In addition to the ability to work with large data sets, familiarity with cloud platforms such as AWS, Google Cloud Platform or Microsoft Azure, which offer tools and services to support data processing (e.g. Amazon S3, Google BigQuery, Azure Data Lake), is also important. The candidate should demonstrate experience in using these environments for scalable data processing and storage.

Experience in designing, building and maintaining ETL (Extract, Transform, Load) processes that enable data to be efficiently moved between systems, transformed and loaded into target data stores or analytics systems is essential. An understanding of best practice and the ability to ensure data quality and purity are essential here.

The data engineer must have an in-depth knowledge of various types of databases, both relational (e.g. MySQL, PostgreSQL) and NoSQL (e.g. MongoDB, Cassandra). Additionally, familiarity with data warehouses such as Redshift, Snowflake or BigQuery is important for effective management and analysis of large-scale data.

Data engineering often requires collaboration with other team members, including data analysts, product managers and development teams. Therefore, strong communication skills and the ability to work as part of a team are crucial to effectively share knowledge and support the delivery of data-driven projects.