What’s in a Job Title? Understanding Changing Data Roles
The world of data is rapidly evolving, and the demand for skilled data professionals has continued to rise. But who are these data professionals? Those of us in the field have been asked many times about the nature of what we do. Students and prospective career changers, hiring managers, business partners, and prospective clients all have questions about what falls within the expertise of a “data professional”.
The answer is not simple.
Data roles are diverse and constantly evolving. Similarly, the lines that separate data disciplines are inherently blurred. The reality faced by active data professionals is also complex, with business and project requirements often requiring them to extend their expertise across disciplines. Consequently, data professionals often wear many hats.
Still, it’s useful for those entering the field or looking to hire a data professional to understand some of the important distinctions between data disciplines.
Here are just a few.
Common Data Roles
Data Architect
Data architects design the overall blueprint for your organization’s data environment. They define how data is stored, organized, integrated, and accessed across systems. They also ensure that your data infrastructure is scalable, secure, architected for efficient retrieval, and aligns with your long-term business goals.
Database Administrator (DBA)
DBAs are the administrators of your database systems. They possess a deep knowledge of the database engine itself, including all its native functionality and features. They are also responsible for keeping databases updated, backed up, secure, and performing optimally. DBAs also manage database upgrades and migrations, as well as database recovery in a disaster or emergency.
Data Engineer
Think of data engineers as the builders of your data infrastructure. They design, construct, and maintain the pipelines that collect, store, and process your data. Their toolkit is diverse and often includes programming languages like Python and SQL. It is also common to leverage cloud platforms like Azure, AWS, or GCP. These professionals ensure that your data is accessible, reliable, and ready for analysis.
Data Analyst / Business Intelligence Analyst
Data analysts are the storytellers of the data world. They take the data that engineers have prepared and draw out insights through creating reports, dashboards, and visualizations. Furthermore, their strong analytical skills and ability to recognize patterns allow them to turn data into knowledge. They tell stories with data to help guide decisions. Tools often include Excel and BI reporting platforms like Tableau or Power BI.
Data Scientist
Data scientists take analytics to a deeper level. They use advanced statistical techniques and machine learning to uncover hidden patterns that are often difficult for humans to detect. Similarly, the algorithms used by data scientists can predict future trends, and the outputs of their models are used to drive decision-making. Data scientists possess mathematical expertise, programming skills in languages like Python or R, and domain knowledge relevant to their industry.
Machine Learning Engineer
Machine learning engineers take the models created by data scientists and make them operational in real-world production environments. They leverage both data science and software engineering skills. ML engineers are responsible for building the systems that deploy, monitor, and scale data science models. They also manage these systems to ensure that they deliver accurate and timely predictions once deployed in a real-world context.
Tips for Choosing Your Future Data Role
Shadowing some different data professionals is a great way to get started. Depending on which role appeals to you, there are different pathways to getting started.
If you have a background in system administration, data architecture and database administration may be good avenues to investigate. Likewise, people who have enjoyed building their own home labs may also enjoy these data roles.
Similarly, if you love automation and problem-solving for technical efficiency, one of the engineering roles may be right for you. Engineers enjoy designing and building solutions for technical challenges.
Finally, if you have a mind for analytics and statistics, an analyst or data science role may be a great fit. These roles uncover the root causes of the problems under investigation in order to unlock potential solutions.
There are a variety of ways to kick-start your journey for any of these data disciplines.
- Self-Education. There are many resources available online that can guide you through learning specialized skills. Set up a home lab environment in which to safely practice. Start to build a profile of projects and/or certifications that can showcase your new skills.
- Bootcamps. The number and variety of bootcamps for data have increased dramatically in the last 5 years. If your schedule allows for participation in one of these intense programs, they can be a great way to upskill rapidly.
- Formal education. If you are looking to go into a highly specialized role in a particular knowledge domain or industry, obtaining an advanced degree can be a great way to get started.
Determining which path to take will depend on what appeals to you and on the circumstances in which you are beginning your journey. The key, however, is just taking the first step.
Tips for Hiring: Which Data Role Do You Need?
Just as the path to becoming a data professional depends on individual circumstances, the type of data professional to hire will also depend on your organization’s needs and where you are in your data maturity journey.
Here are some hiring considerations for several common scenarios:
Just Starting Out
If you are building your data capabilities from the ground up, a data engineer with versatile skills is a great first hire. While partnering with system administrators and business experts, as well as potentially seeking external assistance from a DBA or data architect, a data engineer can lay the foundation for your future data work.
Business Optimization / Improved Operations
If you are looking to use your data for improved operations, a data or business intelligence analyst can work with business partners to track key metrics, identify trends, and drive data-driven decisions to find ways to improve. Likewise, by developing an understanding of the business, this person can work as a bridge between business and IT teams to help harness the full potential of your data.
Complex or Outdated Data Environment
A data architect can bring order to chaos by ensuring that your data is well-organized, accessible, and scalable as your organization grows and expands. Data architects can also be instrumental in reshaping legacy structures to meet current business requirements and make the best use of modern technologies.
Predictive Insights and Automation
Data scientists and machine learning engineers are ideal for building models that can make important predictions, optimize processes, and/or automate complex AI tasks. It is important to note, however, that having a robust, well-governed data infrastructure is a prerequisite for success for these types of initiatives.
Diverse Needs, Large-Scale Projects, or Complex Remediation
Partnering with an external data team with diverse areas of expertise can be an affordable way to: remediate complex problems, make infrastructure improvements rapidly, or design and implement large-scale solutions. Leveraging this type of support also allows you to access professionals with expertise in different data roles and may offer flexibility for scaling up or scaling down your level of support as needed.
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