In-Demand Skills to Look for When Hiring Data Engineers and Data Analysts

Are you hiring a data engineer or data analyst to join your team? Finding the right hire can be tough.

93% of UK businesses say they are experiencing a gap in the tech and data skills they need and the ones they have. Because of this data recruitment is extremely competitive with businesses across different industries and sectors vying for candidates with the skills and expertise to take their data initiatives and projects to the next level. So, before you begin hiring, it’s important to ask one key question; what makes a great data engineer or data analyst?

The answer to that question might change slightly depending on your industry, project needs or company culture. These factors combined with your budget, pay scale and market trends will have a huge impact on how you go about hiring data engineers and analysts. You can find out more about the trends behind tech hiring and salaries with our 2024 Data & Analytics Salary Guide.

In general, you should be hiring data engineers and data analysts who excel in three main areas, technical proficiency, problem solving ability, and the ability to communicate effectively and collaborate with others. With that in mind, here are the most in-demand skills to look for when you are hiring data engineers and data analysts:

 

Top Data Engineering and Data Analyst Skills to Look For

The skills and expertise you look for when hiring data engineers and data analysts will vary depending on the specific technology you use in your tech stack, and the type and size of your company. You should always be careful to screen and evaluate candidates based on your unique requirements.

You should also be looking for candidates with strong soft skills as well as an interest in new technologies such as AI and how they can incorporate them into their practice.

 

Top Data Engineering Skills and how to identify them

However, there are some skills which will be relevant no matter what. When hiring data engineers, screening and interviews should be focused on critical skills such as:

1) Programming and Coding

When seeking candidates with strong programming and coding skills, focus on several key areas: proficiency in relevant coding languages, problem-solving capabilities, optimisation skills, and adherence to best practices. Assessing these areas requires a combination of strategies.

Interviews should be a mix of technical questions and problem-solving scenarios that test both theoretical knowledge and practical application. For example:

• Python: “Can you explain how you would use Python’s list comprehensions to simplify a loop? Show an example.”
• R: “How would you handle missing data in a large dataset using R? What functions or packages would you use?”
• SQL: “Describe how you would optimise a SQL query that’s running slowly. What strategies or tools might you use?”
• Scala: “How do you handle immutability in Scala, and why is it important? Provide a code snippet illustrating your approach.

Use questions like these alongside detailed discussions of candidate’s previous projects and contributions. Ask about the specific challenges faced, the technologies used, and how they handled various aspects of the project.

You can also use coding challenges or take-home assignments relevant to your project or industry. These should test the candidate’s ability to write functional, efficient code. For instance, design a small project or algorithmic problem related to your industry and evaluate how candidates approach it.

2) Data Pipeline Development

When evaluating candidates for data pipeline development skills, it's essential to assess their proficiency in data extraction, transformation, and loading processes, along with their ability to build robust and scalable pipelines. Key areas to focus on include knowledge of ETL tools, data modelling, pipeline automation, and performance optimisation. Questions should be designed to establish both a candidates theoretical understanding and practical expertise. For example:

• ETL Tools: “Can you describe a scenario where you used an ETL tool like Apache NiFi, Talend, or Informatica to streamline data integration? Provide an example of a specific workflow you implemented.”

• Data Transformation: “How do you approach transforming raw data into a format suitable for analysis? Give an example of a complex transformation you performed and the challenges you faced.”

• Pipeline Automation: “Explain how you would automate a data pipeline using tools like Apache Airflow or Luigi. What are the key components you would include to ensure reliability and scalability?”

• Performance Optimization: “What strategies would you use to optimise the performance of an ETL pipeline handling large volumes of data? Describe an instance where you significantly improved pipeline efficiency.”

You may also consider using assignments that test their ability to design and implement efficient ETL processes. For example, provide a dataset and ask the candidate to create a pipeline that extracts, transforms, and loads the data into a specified format, evaluating their approach and execution.

3) Database Management

Effective database management ensures that data is stored, retrieved, and maintained efficiently. Candidates should have a working knowledge of both relational databases (such as MySQL and PostgreSQL) and NoSQL databases (like MongoDB and Cassandra). Experience in database design, optimisation, and management is crucial for maintaining data integrity, performance, and scalability.

In interviews, you can ask candidates about the specific challenges they faced with database management, such as handling issues related to schema design, query optimisation, and resource management. Additionally, include questions specific to the individual technology you are assessing. Questions like:

• MySQL: “How would you design a schema for an e-commerce application? What considerations would you consider for ensuring data integrity and efficient querying?”
• PostgreSQL: “Describe how you would use PostgreSQL’s indexing features to improve query performance. Can you provide an example of when and why you might use a B-tree index versus a GIN index?”
• MongoDB: “How would you model a many-to-many relationship in MongoDB? What strategies might you use to handle data consistency and performance?”
• Cassandra: “Explain how Cassandra’s data model and architecture support high availability and scalability. How would you design a schema for a write-heavy application?”

 

Top Data Engineering Skills and how to identify them

When hiring data analysts, alongside your unique project and tech requirements you should also be tailoring your process to look for competencies in:

1) Data Analysis and Visualisation

Strong analytical skills are essential for data analysts as they need to interpret complex data sets and derive meaningful business insights. Proficiency with tools like Excel, R, and Python are crucial for data manipulation, statistical analysis, and advanced analytics. Candidates must also be able to visualise data and effectively communicate their findings to support decision-making.

Questions should test candidates’ analytical capabilities and visualisation skills tailored to the tech you use, specifically their ability to:

• Query the database: “Can you write a SQL query to extract the top 10 performing products by sales in the last year? How would you handle potential performance issues with this query?”
• Produce quality visualisation: “How would you use Tableau to create an interactive dashboard for sales data? What types of visualisations would you include, and why?”
• Derive insights: “Given a dataset with customer transaction data, how would you identify trends and insights that could inform a marketing strategy? Walk me through your approach.”

2) Communicating Key Insights

Your hiring process needs to also assess a candidate's ability to effectively communicate their insights. This includes their ability to influence decisions and drive actions based on data-driven insights.

Establish their experience in presenting data to various audiences and their skill in tailoring their communication to different stakeholder needs by presenting a dataset and ask candidates to perform an analysis on the information and create a visualisation that highlights key insights. They should then be able to translate their findings into actionable business recommendations.

For further help and advice around hiring data engineers or data analysts for your data initiatives contact David Pynor today. We specialise in helping organisations like you recruit for mid-senior level, interim and permanent roles across Data & Technology.

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