Big Data Analytics: Career Prospects

There is a wealth of employment opportunities for professionals with knowledge of big data analytics. This post explores what employment opportunities currently exist for people graduating with a concentration in data analytics.

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Masters and Doctoral graduates have some advantages over Undergraduates, because they have done research or capstones involving big datasets, they can explain the motivations and reasoning behind the work (chapter 1 & 2 of the dissertation), they can learn and adapt quickly (chapter 3 reflects what you have learned and how you will apply it), and they can think critically about problems (chapter 4 & 5 of the dissertation).  Doctoral student, work on a problem for multiple months/years to see a solution (filling in a gap in the knowledge) that they couldn’t dream of seeing as incomplete (or unfillable).  But, to prepare best for a data science position or big data position, the doctoral shouldn’t be purely theoretical, and should contain an analysis of huge datasets.  Based on my personal analysis, I have noticed that when applying for a senior level position or a team lead position in data science, a doctorate gives you an additional three years of experience on top of what you have already.  Whereas if you lack a doctorate, you need a Master’s degree and three years of experience to be considered for that senior level position or a team lead position in data science.

Master levels courses in big data help build a strong mathematical, statistical, computational, and programming skills. Doctorate level courses help you learn and push the limits of knowledge in all these above mentioned fields, but also aid in becoming a domain expert in a particular area in data science.  Commanding that domain expertise, which is what you get through going through a doctoral program, can make you more valuable in the job market (Lo, n.d.).  Being more valuable in the job market can allow you to demand more in compensation.  Multiple sources of can quote multiple ranges for salaries, mostly because, this field has yet to be standardized (Lo, n.d.).  Thus, I would only provide two sources for salary ranges.

According to Columbus (2014), jobs that involve big data could include Big Data Solution Architect, Linux Systems and Big Data Engineer, Big Data Platform Engineer, Lead Software Engineer, Big Data (Java, Hadoop, SQL) have the following salary statistics:

  • Q1: $84,650
  • Median: $103,000
  • Q3: $121,300

Columbus (2014) also stated that it is very difficult to find the right people for an open requisite and that most requisites remain open for 47 days.  According to Columbus (2014), the most wanted skills for analytics data jobs based on of requisition postings in the field are: in Python (96.90% growth in demand in the past year), Linux and Hadoop (with 76% growth in demand, each).

Lo (n.d.) states that individuals with just a BS or MS degree and no full-time work experience should expect $50-75K whereas data scientist with experience can command up from $65-110K.

  • Data scientist can earn $85-170K
  • Data science/analytics managers can earn $90-140K for 1-3 direct reports
  • Data science/analytics managers can earn $130-175K for 4-9 direct reports
  • Data science/analytics managers can earn $160-240K for 10+ direct reports
  • Database Administrators can earn $50-120K, which varies upwards per more experience
  • Junior Big data engineers can earn $79-115K
  • Domain Expert Big data engineers can earn $100-165K

One way to look for opportunities in the field that are currently available is looking into the Gartner’s Magic Quadrant for Business Intelligence and Analytics Platforms (Parenteau et al., 2016). If you want to help push a tool into a higher ease of execution and completeness of vision as a data scientist consider employment in: Pyramid Analytics, Yellowfin, Platfora, Datawatch, Information Builders, Sisense, Board International, Salesforce, GoodData, Domo, Birst, SAS, Alteryx, SAP, MicroStrategy, Logi Analytics, IBM, ClearStory Data, Pentaho, TIBCO Software, BeyondCore, Qlik, Microsoft, and Tableau.  That is one way to look at this data.  Another way to look at this data is to see which tools are the best in the field and (Tableau, Qlik, Microsoft, with SAS Birst, Alterxyx, and SAP following behind) and learn those tools to to become more marketable.

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