Hire deeply-vetted data engineers

Hiring a data engineer is easy, but hiring the right engineer with adequate data-related experience and knowledge is difficult. Companies that want to find valuable insights from raw data and develop dashboards and visualizations for stakeholders must hire the best data engineers.

However, hiring data engineers is getting increasingly challenging. Companies are competing to hire from a limited talent pool, making it costly and time-consuming to hire remote data engineers.

Turing aims to simplify things with our AI-powered talent cloud that leverages global reach to source high-quality engineers in 4 days. We use deep vetting to select the best data engineers after evaluating their communication skills and technical expertise in cloud computing, big data analytics, data modeling, data security, and other key areas.

Moreover, if you are a non-technical manager interested in learning more about the recruiting process to hire remote data engineers, we've put up an excellent resource for you.

At a high level, data engineers should have the following skills in their arsenal:

The knowledge of relational databases like PostgreSQL or MySQL, and NoSQL databases like Cassandra or MongoDB is crucial for expert data engineers. They must understand concepts like database administration, query optimization, and performance optimization to perform data-related tasks efficiently and as per business needs.

Version control systems like Git, SVN, Mercurial, etc., assist data engineers in building and developing applications safely and efficiently. These tools help them keep track of each step while building applications. Besides this, code versioning tools also allow engineers to experiment with various new application features, which helps create robust applications. Data engineers who have adequate knowledge of working with such platforms can work efficiently on any project.

Expert data engineers are proficient in designing and implementing robust data models that meet organization requirements. This involves understanding normalization techniques, data structures, and database design principles. Plus, a skilled data engineer must also have expertise in the extraction, transformation, and loading (ETL) of data from different sources into a unified data repository.

In order to precisely and efficiently assess data, engineers need a highly scalable infrastructure that offers a high-performance data storage mechanism. That's why when hiring, ensure to check the candidate's cloud computing capabilities. Skilled data engineers are proficient in cloud-based processing and data storage platforms like Microsoft Azure, Amazon Web Services, or Google Cloud Platform. They must also be familiar with Amazon S3, Azure Data Lake, Redshift, Google BigQuery, and other relevant services for efficient programming.

The role of a Data Engineer involves performing complex data analysis to find patterns in large chunks of data and then reporting it in the form of visualizations. Big data analytics help in harnessing the data and utilizing it to identify future opportunities in the business. Hence, when you hire data engineers, check their expertise in distributed computing frameworks like Apache Spark, Apache Hadoop, and Apache Kafka, and their working knowledge of big data processing engines like Hive, Pig, or MapReduce.

When hiring data engineers, be sure to assess their data warehousing skills. Data engineers must have a solid understanding of designing and creating data marts or warehouses for efficient data retrieval, storage, and analysis. Expert data engineers are well-versed in concepts like data cubes, star schema, and dimensional modeling.

Creating a hiring funnel will provide you with numerous benefits, like assisting you in identifying the top skills and identifying a data engineer who will fit into your company's culture.

Whether you're an IT recruiter or a project manager, you know that hiring top data engineers is critical to the success of your project. Here are some sample interview questions to use when looking for a new data engineer to work on your online applications.

Here are some more data engineer interview questions that you can ask to assess a developer’s caliber.

• Difference between NAS and DAS in the Hadoop Cluster.
• What is the replication factor in HDFS?
• What is MapReduce in Hadoop, and what role does Reducer play?
• What happens when the Block scanner detects a corrupt data block?
• Is building a NoSQL database better than building a relational database?