Business analytics play a key role in business as more companies and other organizations rely on data to analyze consumer behavior, solve problems and operate more efficiently.
Organizations depend on information gathered from customers, vendors, competitors, economic reports and other sources to decide factors such as what to sell, when to sell, where to sell and to whom to sell. Insights mined from relevant data can help organizations deliver services more efficiently, whether it’s improving traffic at a fast-food drive-up window or sourcing materials during pandemic-related supply chain disruptions.
However, without the people to manage it, data is nothing more than random numbers, words and images piling up in a database. Data mining engineers and data scientists wrangle data into systems and formats that enable data scientists to add context and produce valuable insights. The demand for data mining engineers and data scientists continues to grow.
What a data mining engineer does is crucial to this process, from input to storage to output. Engineers build the infrastructure that moves data to the right place and in the right format to be analyzed. Because they touch each part of the process from beginning to end, data mining engineers with skills and experience are poised to move into process- and policy-setting roles in a range of industries.
What Does a Data Mining Engineer Do?
Data mining is the process of sorting through information to produce findings that a business can analyze to improve systems and operations. Data is of little use without careful manipulation and presentation.
What a data mining engineer does is set up and operate the infrastructure for storing and analyzing data. Overarching tasks include setting up data warehouses, organizing data so it’s accessible and installing conduits for data to flow through. A data mining engineer must know the source of the data, how it’s going to be used and who is going to use it. The key acronym for a data mining engineer is ETL: extract, transform and load.
A data mining engineer should possess a deep knowledge of data software systems and programming languages and be able to choose the tools that best serve their data tasks. At the top of the list is the SQL (structure query language) database programming language, which is widely used to store and access data in a database. Data mining engineers also use Python, Java, R and MapReduce, as well as systems such as Apache.
Familiarity with major cloud computing platforms, such as Amazon Web Services, Microsoft Azure and Google Cloud, is also required, as is the ability to leverage analytics tools such as Pandas and PySpark.
Business and Communication Skills
Besides strong technical knowledge, a data mining engineer should have a firm grasp of their company’s business to ensure that mined data meets its needs. The more focused the data mining process, the better an engineer can answer questions and identify patterns to benefit the company.
A data mining engineer is an advocate for both the database system and its manager. They advise company executives on the best equipment and software to meet the company’s needs and look for opportunities to improve the system and increase its relevance to company goals. This calls for good communication skills and the ability to present technical matters in a way nontechnical colleagues can also comprehend.
How to Become a Data Mining Engineer
Data mining engineers usually have an undergraduate college degree, preferably in a field such as software engineering, computer science, mathematics or engineering. These disciplines provide a strong foundation for becoming a data mining engineer. Having some education in the field in which one’s employer does business is also helpful to understanding its needs.
Earn an Advanced Degree
An advanced degree such as the University of Nevada, Reno’s online Master of Science in Business Analytics can expand and sharpen the skills required for a career in data mining. Pertinent courses include Data Transformation and SQL, Visualization and Communication, Applied Data Science, and Predictive Modeling.
Develop Technology Skills
Beyond education, employers prefer candidates with work experience with database design programs, such as SQL, and programming languages used in database work, such as Python and Java. Employers often look for data mining engineers with experience in business intelligence tools, such as Cognos, Tableau and Sisense.
Certifications that demonstrate knowledge in general data analytics, as well as in specific environments, can add to a data mining engineer’s resume. The Data Science Council of America (DSCA) offers certifications unrelated to specific platforms. For an associate big data engineer certification from DSCA, the candidate must pass a test of some 70 questions demonstrating knowledge of data mining concepts, as well as some popular platforms and languages. Data engineers with more experience can earn the senior data engineer certification with a longer, sterner test.
Specific data platform vendors also offer certifications to engineers who pass exams on the vendors’ products. Some vendors recommend that candidates take specific courses to prepare for the exams. Vendor certifications include:
- Cloudera Certified Associate (CCA) Data Analyst
- EMC Proven Professional Data Scientist Associate (EMCDSA)
- IBM Data Science Professional Certificate
- Microsoft Certified Azure Data Scientist Associate
- SAS Certified Advanced Analytics Professional Using SAS 9
- Google Cloud Certified Professional Data Engineer
A Sturdy Foundation
Data mining engineers are well-positioned to move into other data-related areas. The role has a wide view of the data retrieval, processing and analysis system. This gives data mining engineers experience in each phase of the process, which can be valuable to have when seeking other data jobs.
Data Mining Engineer Salary
The median annual salary for a data mining engineer is about $89,000, according to May 2021 PayScale data. Salaries can be higher depending on factors such as education and experience.
The biggest salary increase comes for data mining engineers with between four and 10 years of experience. Data mining engineers with around four years of experience have a median annual salary of about $84,000. Add about five years of experience, and the median annual salary increases to approximately $104,000.
Skills that bring increased remuneration include SQL, Python, ETL (extract, transform, load) and Apache Spark. Other skills that have a positive impact on salary include MapReduce, Amazon RedShift, Apache Cassandra and Scala.
Location also affects a data mining engineer’s salary. Salaries in Seattle, home to Amazon and Microsoft, are about 20% higher than the national average. Salaries also are higher than the national average in New York City and Los Angeles.
Harness the Power of Big Data
Businesses and other organizations have more data than they know what to do with. Taking advantage of the power of big data can make a business more efficient, more competitive and more responsive to customers, but organizing that data requires data mining engineers. These professionals set up the systems that capture, analyze and depict data in ways that are meaningful to a business.
Pursuing a career as a data mining engineer begins with a strong foundation in business, data and analytics. The online Master of Science in Business Analytics program at the University of Nevada, Reno is designed to ready you to contribute to organizations in today’s fast-changing business landscape. With courses in predictive modeling and data mining, information visualization, and applied data science, you can gain the real-world knowledge needed to turn raw data into valuable insights.
Explore the University of Nevada, Reno and take a step into your future today.