Hiring a data analytics engineer: what to look for

Cole Lehman -

Data analytics engineers play a central role on the teams that turn raw data into actionable business insights and metrics. It’s one of the most in-demand software engineering roles for companies that want to keep up with the business trends in data and analytics—e.g. faster AI, decision intelligence, and cloud data.

Companies across industries from fintech and healthcare to ecommerce and entertainment are actively recruiting data engineers to work on data analytics, build data pipelines, and maintain data tools. Do you need to hire one? Probably, and it’s important to know what to look for in a new candidate.

Here’s an overview of everything you need to know about what a data analytics engineer does for your business and how to hire a good one.

What is a data analytics engineer?

A data analytics engineer is a DataOps professional that uses data tools and analytical skills to turn your data into actionable insights. They can help you build a modern data stack, set up data pipelines and transformation tools that gather, process, and analyze data from all available sources, and more.

Data analytics engineers are a unique combination of software engineers and data analysts. Depending on the size of your company and the maturity of your data analytics team, a new data engineer might focus only on analytics or they could take responsibility for your whole data pipeline.

Ideally, you have enough data engineers, data scientists, machine learning engineers, and data analytics engineers to spread out responsibilities. But if you’re just starting to build your data team, you can still find a single data analytics engineer that’s a good fit for your business needs.

Here’s a closer look at what data analytics engineers do every day.

What does a data analytics engineer do?

The primary responsibility of data analytics engineers is to gather and clean large sets of data for business teams. They use a mixture of SQL, git, and low-code tools  to load quality datasets and build useful data models. A data analytics engineer can also set up data automations to feed company workflows and dashboards across departments.

Their day-to-day involves everything from working with data profesionals to create plans for data ingestion changes to sitting in a room full of business users trying to come up with a new data model for lifetime customer value.

Here’s a short list of what data analytics engineers work on:

  • Building and automating data workflows
  • Eliminating data silos
  • Monitoring data transformation and data ingestion pipelines
  • Delivering accurate data, on time
  • Building data visualizations, reports, dashboards for metrics, and advanced analytics capabilities
  • Managing data solutions and technology decisions
  • Managing data tools and data warehouses—e.g. Shipyard, Snowflake, Fivetran, Stitch, dbt, Redshift

Data analytics engineers also work closely with data engineers and software developers to develop methods for collecting, storing, and analyzing huge volumes of data quickly and efficiently. Ideally, your data analytics engineers can switch from speaking technical DataOps terminology to non-technical business vernacular. This way, they can work with stakeholders from all parts of your company.

Your company can expect significant benefits even if you only have one data analytics engineer on your data team.

Tips for a Career in Analytics Engineering, From an Analytics Engineer

What benefits come from a data analytics engineer?

Actionable insights, improved data quality, and fewer data silos—these are just some of the benefits your company gets with a skilled data analytics engineer. They're responsible for everything from designing your cloud data warehouse to devising ways to visualize complex datasets so that business users can easily locate the trends and insights they need.

With a data analytics engineer on your data team, more people across every part of your business get better operational analytics. Here are some of the overall company benefits you can get with a data analytics engineer added to your data team:

  • Streamline compliance and data governance
  • Make decisions faster, collaborate better, utilize sharper business intelligence, and lower costs associated with storing data
  • Create new sources of revenue and new products out of new use cases discovered in big data patterns
  • Deliver accurate data on time, every time
  • Improved data quality
  • Combine data from a variety of sources in an automated way
  • Use data analytics tools more effectively, reduce costs by eliminating redundancies, and save time with automated data management
  • Innovate more rapidly than ever before through self-service access to consolidated data
  • Increase data privacy and data security across the business

A good data analytics engineer can also help your teams spot trends and patterns in an industry or product market—an invaluable benefit when it comes time to decide which opportunities to prioritize.

So if these are all things you want for your company, it’s time to start searching for a data analytics engineer to hire. Before you start writing the job post, make sure you know how this person fits with your existing data team.

How does a data analytics engineer fit on your analytics team?

Data analytics engineers are invaluable members of any analytics team because they bridge the gap between DataOps and Business Stakeholders. They work closely with both groups and have a deep understanding of how each area operates, which helps them identify potential issues before they become problematic.

Their primary goal is to help people make more informed decisions by providing them with real-time information on their environment's status, as well as statistics on historical trends that can be used when making future forecasts.

A data team typically consists of the following:

  • Data engineers: Data engineers are responsible for moving data from source to source, building data workflows, data governance, and maintaining solutions like cloud data warehouses and data lakes. Data engineers also help build and maintain extract, transform, and load (ETL) and extract, load transform (ELT) processes.
  • Data or Business Intelligence (BI) Analytis: Data analysts are responsible for analyzing large volumes of data. They interpret data, study reports, and help identify trends that lead to strategic decision-making. They are often times responsible for building out the very dashboards that everyone on the team looks at for insights.
  • Data scientists: They are experts in statistical models who understand both business and data technology. Data scientists advise their business stakeholders on how data can bring strategic value, and build out algorithms and models to help achieve that value.
  • Machine learning engineers: Machine learning engineers are in charge of developing algorithms, improving existing algorithms, and modifying those algorithms to perform better.
  • Chief data officer (or other decision-maker): A chief data officer is a C-level executive responsible for leading data strategy, implementation, and optimization. Their job involves using data insights to shape corporate strategies and drive business performance.

A data analytics engineer is a cross between a data engineer and a data analyst. They know which business questions the data needs to answer, and they work with your whole data team to roadmap and build the right solutions. Data analytics engineers know how to build, run, and monitor workflows that result in clean data. They also know how to present data to business stakeholders in a relatable, actionable way.

In some cases, a data analytics engineer may require a degree in computer science. In others, you’ll have a data scientist who’s already got that covered. Your data team will be unique and you need to understand the current mix of skills across your existing role before you add a new person.

What qualities should every data analytics engineer have?

They must possess strong analytical skills, programming knowledge, and the ability to understand complex data structures quickly. The job typically requires at least a few consecutive years’ experience using tools like git and SQL. It's always helpful for them to have experience with programming languages such as Python or R. And you can't go wrong with advanced formula knowledge for tools like Excel.

To do their job effectively, they'll need excellent written communication skills—especially if you want them to serve as your company's spokesperson internally or externally.

And that’s just some of the qualities every data analytics engineer needs to have. Here’s a short list of the common qualities you want in a new role:

  • Ability to code: Python, R, SQL (or all three)
  • Transalte business requirements into data structures
  • Translate data into meaningful insights
  • Communicate effectively verbally and in writing
  • Quick learners with excellent problem-solving skills
  • Patient, organized, and detail-oriented

They also need experience with various types of data tools, data pipeline technologies, data warehouse solutions, and business analysis processes. Knowing what technologies your data team needs determines which candidate should be hired for this position.

In short, the qualities needed in a good data analytics engineer vary from company to company. But, there are consistent things you can do to find a good candidate no matter what your business requirements are.

How do I hire a good data analytics engineer?

They’re in high demand so give yourself some time to search. Data analytics engineers often have several years of experience under their belts before going into this line of work, including some formal training in mathematics or computer science. Not everyone has the same journey though - some of the best data analytics engineers are individuals who were in a business role that gradually gained technical data skills through necessity.

When you start your search, look for candidates with years of relevant experience and interest in making data actionable. Many of them start out as data analysts or DI developers before transitioning over to this more specialized field.

These are the best places to begin your search:

Of course, you’ll have to write a job description and post it on your company website. But that’s not going to be enough in this market. Ask around, look on LinkedIn, and go to some conferences. With a little effort, you’re sure to find a data analytics engineer that’s a good fit for your company.

What’s next?

Do you really need a data analytics engineer or just a data analyst? Make sure you’re choosing the right fit for your data analytics team. And if you don’t know after reading this article, we’re happy to help you understand who you need to hire.

Shipyard builds and provides tools for data teams to create solid data infrastructures that connect data stacks from end to end. Our team is happy to help you wrap your head around anything in this article, just send us a message.