This blog is contributed by Burtch Works’ data engineering recruiting team
With hiring on data science & data engineering teams continuing strong throughout 2021, our team has noticed several types of roles that are blending the two fields. We’ve written before about how we typically identify data scientists and data engineers, but wanted to share some of our observations on other types of roles that have been incorporating skillsets from both.
Blended and Hybrid Roles in Data Science & Data Engineering
1. Data Science Engineer
These professionals may lead or partner with data scientists with the goal of building an environment that enables analysis of vast amounts of data. They typically will have experience with both software engineering and data science, including experience with cloud platforms and the full software development lifecycle, as well as data analysis tools. This enables them to work side by side with the data science team with the goal of building scalable, user-friendly systems and tools for internal customers.
2. Analytics Engineer
From what we’ve seen so far, these professionals are in between data engineers and data analysts. Based on the job descriptions that we’ve seen, analytics engineers may be working more with clean datasets as well as modeling data, with the goal of helping end users answer their own questions. This may include maintaining documentation and definitions, and then training business users on how to use data visualization tools.
Their skillset is typically a mixture of both data engineering and analytical modeling, and, in this type of blended role, analytics engineers will need to understand how data insights impact business goals instead of mostly focusing on building infrastructure. Traditionally, data engineers are going to be helping build up infrastructure or preparing data for the data science team, but analytics engineers may sometimes even be part of a separate team entirely from both the data scientists and data engineers.
3. Machine Learning Engineer
While we may have once referred to ML Engineers as a specialization within data engineering, this role has become prevalent enough that it is a role all its own. Depending on the specific demands of the role, professionals in this type of hybrid position may have more of a typical data engineering background or more of a data science background, but they will have enough experience to be proficient in both fields. Because of their hybrid nature, these roles can vary depending on the organization, but are typically assisting data scientists, data engineers, and dev ops engineers.
Machine learning engineers will have a deep understanding of and experience with applying machine learning concepts, as well as deploying, scaling, monitoring, and continually improving algorithms. This often includes optimizing models from the data science team and preparing them for deployment, creating the last part of the data science pipeline. They will have significant experience with deploying models as well as building production data and machine learning pipelines.
4. DevOps/Data Engineer
With the rise of the machine learning engineer, many teams have been looking for engineers who are adept at cleaning data as well as building machine learning pipelines and putting models into production. However, we’ve also seen some companies that have been looking for data engineers who can effectively test and troubleshoot in a production environment, which is traditionally considered more DevOps than data engineering. We pointed this out in our predictions earlier this year, as we started to hear about more data engineers being asked to take on this type of work. While this type of role may not be as widespread yet as others on this list, that may change in the future.
5. Data Analyst
While this is a job title we often see on traditional analytics teams, these roles can sometimes lean further towards data engineering. This may include data analysts who are experienced in data cleaning, analysis, and creating visualizations, but who also assist the data engineering team on ETL pipeline builds or prepping work for the engineering team. This usually requires a data analyst to have more of an information systems/technology background than the math/statistics background that we see in a typical data analyst profile. In some cases, we’ve even seen this role include professionals that might be better described as Tableau Developers who are mainly focused on building visualization reports, but still have the Data Analyst title.
Like any job title, these roles can vary between different organizations, and will likely continue to shift over time as the needs of data teams evolve and grow. Some roles may even change according to a planned schedule over time, including one example where the first few months were to be heavily focused on BI tech, and then transforming to a more analytical role later on in order to drive business insights based off an established analytical capability.
Overall, it has been fascinating to watch this space continue to shift as new technologies and methodologies are implemented at more organizations. As recruiters, seeing new types of roles and specializations is an interesting part of what we do, so we’ll continue to keep you updated on developing trends as we see them taking shape.
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