Looking for an in-demand career path?
Big Data positions are in high demand. In particular, two high-paying positions that enterprise and research institutions are seeking are data scientists and data engineers.
Let’s compare the key differences in the roles and responsibilities of a data scientist and a data engineer to help you decide which one is right for your career.
Big Data is defined by Merriam-Webster as, “an accumulation of data that is too large and complex for processing by traditional database management tools.”
With data sets this large, new roles have emerged to manage, query, interpret and visualize the data. Additionally, the data then needs to be turned into actionable insights to help business and organizations. The roles of data scientist and data engineer are found in large, enterprise corporations and organizations, as well as research and governmental institutions. Both positions have enormous growth potential and have been featured on Glassdoor’s 50 Best Jobs in America for three consecutive years.
Here Are the Key Differences in These Two Roles:
Salary, rank and job openings provided by Glassdoor’s 2018, 50 Best Jobs in America ratings.
Data Scientist, Medium Base Salary: $110,000
Glassdoor Rank: 1
Current Job Openings: 3,369
Data scientists turn clean, raw data into actionable insights. They accomplish this by applying statistics, machine learning and analytic approaches to solve critical business problems. Data scientists contribute to data mining architectures, modeling standards, reporting and data analysis methodologies. Their ability to collaborate with stakeholders to integrate big data results into existing business systems and scenarios is a critical component of their role.
Data scientists are often integrated within a business unit/product team that will utilize their research, or they can be part of a business intelligence (BI) or engineering team.
Data Scientist Skills:
Technical Skills: Programming, mathematics, business acumen, statistics, data visualization, machine learning, experience with big/small data sets
Soft Skills: Detailed, strong verbal and written communication skills, ability to explain complex technical scenarios to nontechnical leaders
Technology: Spark, Apache Mahout, graph databases, Tableau
Career Path: Junior data scientist →Data Scientist → Senior Data Scientist → Chief Data Scientist
Data Engineer, Medium Base Salary: $100,000
Glassdoor Rank: 33
Current Job Openings: 2,816
Data engineers prepare the “big data” infrastructure to be analyzed by data scientists. They design, build, integrate, test and maintain big data from multiple resources. Additionally, data engineers write complex queries that optimize the performance of their organization’s big data ecosystem.
Data engineers also design big data warehouses that can be used for reporting or analysis by data scientists. A data engineer’s focus is on the design, architecture and output of the data. Once there is a data pipeline, the data scientist is responsible for the analysis and outcome of the results. Data engineers work closely with data scientist and are sometimes part of the same team.
Technical Skills: Database design, production coding, data collection, data warehousing, data transformation
Soft Skills: Mechanical tendencies, patience, humility, focus
Technology: Hadoop, MapReduce, Hive, Pig, data streaming, NoSQL, SQL, DashDB, MySQL, MongoDB, Cassandra
Career Path: Data Engineer → Senior Data Engineer → BI Architect → Data Architect
Summary: Data Scientist and Data Engineer
Data engineers prepare, clean and optimize the data, while data scientists take the data and perform analyses and visualization techniques to understand and explain the data.
Which Career Path Is Right for You?
Careers in big data, whether they be data analyst, data engineer or data scientist are in high demand. The career experts at ASK Staffing have big data positions available, take a look and start a new career path today.