As more organizations become data-driven, the demand for capable data professionals to support and advance their initiatives has never been greater. Employment in computer and IT occupations is expected to grow 15% from 2021 to 2031 – significantly faster than the average for all occupations. What’s more, data-centric roles rank among the best jobs for earning potential, job satisfaction, and position availability. Which direction should you take when building a career in data? Below, we’ve highlighted a few top Data Management careers to consider, as well as the typical responsibilities, skills, and education required for each.
A data architect works with data at the most comprehensive level, translating the organization’s overall data strategy into an effective data architecture. Data architects work with different departments and stakeholders to design and create the enterprise data management framework.
Some of the most popular skill sets for data architects include coding languages, SQL, ETL/ELT operations, database work, and data warehousing. Data architects must also understand data modeling and have experience with business intelligence work.
The data architect role typically requires an undergraduate degree in computer science, as well as several years of experience in a related field. Obtaining a professional credential such as the IBM data architect certification will also help you stand out.
The data analyst’s job is more about getting data from a repository and putting it to good use – by developing business intelligence or assisting with key business operations. Data analysts will typically extract and analyze data using SQL and other tools. They might also maintain a database or multiple database structures.
Because data analysts will often work with raw, unstructured data – which lacks the cohesive elements that make it ready for use – their skills will be partially related to cleaning, extracting and refining data. Data visualization is a key skill for this role, as well as experience with statistics.
In addition to many certifications available for data analysts, they must also have a bachelor’s degree in computer science or a related field. Check out this 12-week program from Cornell as an example of supplementary or starter credentials.
A data manager maintains an organization’s data according to established policies and procedures. Why is this important? So much of the work that is done with data has to conform to a set of policies. This helps with compliance to industry or agency standards and can also protect against the high cost of data breaches or data leaks.
Data managers often catalog data for the business and work according to a data governance program (or may even help develop that program). As data regulations such as the European Union’s GDPR and California’s CCPA go into effect, the data manager provides guidelines on how to handle and process sensitive data sets.
A data manager should have at least a bachelor’s degree in a computer-related field (with some roles requiring an MBA), as well as some experience with data governance, data stewardship, quality assurance, and cybersecurity management. Knowing SQL is also a necessary skill at this job level.
The role of data scientist may be confusing to those who are not in the industry. In the roles of data analyst and data scientist, there is some overlap. Like data analysts, data scientists prepare and analyze data. However, data scientists tend to focus more on using scientific methods to work with data, whereas data analysts work more on interpreting and presenting it.
Some of the key responsibilities for data scientists include data mining, using machine learning programs, and working with structured and unstructured data according to certain kinds of data science like classification, linear regression, or even neural network models.
Data scientists must have programming skills, such as knowledge of Java or C++, and experience with statistics. A data scientist is also well positioned if he or she understands key concepts in machine learning algorithms. For example, a data scientist who understands nearest neighbors and decision forest models is an asset to an organization.
Most data scientists have an undergraduate computer science degree, and according to Zippia, one-third of data scientists have a master’s degree as well.
Data Governance Lead
The data governance lead works with data governance across multiple domains, responsible for creating and communicating policies and procedures.
A data domain is simply a logical grouping of data in some way. Different data domains may include different architectures that work like silos to isolate key data from other applications. Typical data domains include customer data, product data, financial data, or certain kinds of operational data in end user systems.
Because data governance leads work across multiple domains, and with multiple departments, they benefit from having leadership skills as well as experience with data privacy rules like CCPA and GDPR.
A bachelor of science degree is recommended for this job role, as well as several years of experience working with big data and/or data governance.
A data modeler works with data models that are streamlined for workflows and operational results, typically focusing on building frameworks for how data moves through a given workflow. Data modelers benefit from having data warehousing, communication, and conceptual skills that will help them develop those models.
Computer science programs can help build this conceptual skill set by having students brainstorm how to code a particular application. In addition, the data modeler should have creative skills and be able to apply that to business operations and business intelligence.
As someone involved in both data pipelines and modeling, the data engineer has a reputation for being the “Swiss Army knife” of the data handling team. They are primarily responsible for building and maintaining the organization’s data architecture systems.
A lot of the data engineer’s work will touch on some of the work done by the above roles in analysis, pipeline development, and more. A data engineer may be involved in acquiring data sets, achieving compliance, developing algorithms, or building, testing, and maintaining data pipelines. Data engineers may also be involved in validating or analyzing data.
Required skills include coding languages like C++ or Python, experience with relational and non-relational database skills, data storage, and ETL. A data engineer who understands machine learning is also important to an organization.
Data engineers should have a bachelor’s degree in computer science or information technology. Online training and professional certifications from institutions such as Cloudera, IBM, and Google can also be helpful.
This list of Data Management careers is only the beginning: From data curators to data stewards to data security analysts, there’s no shortage of job roles to pursue. Regardless of the path you take, learning how to use, manage, and make the most of data will position you for success at any data-focused enterprise – both now and in the future.
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