6 Career Advancement Tips for Big Data Professionals

Companies across the globe continue to seek qualified big data experts to help drive their success. These jobs are competitive – but the payoff comes in highly challenging work and equally attractive compensation.

• Forbes recently reported the median average salary for an experienced big data professional at $124,000 a year. This applies to positions including software engineer, platform software engineer, information systems developer and data quality director.
• Top industries hiring big data-related experience include professional, scientific and technical services; information technologies; manufacturing, finance and insurance, and retail trade.

Use these guidelines to find a great job or advance your career in the big data sector:

1. Have a solid statistical background. A solid quantitative background is the basis of a successful big data career. If you have a strong handle on quantitative reasoning and a degree in a field like mathematics or statistics, you are well on your way. Add in expertise with a statistical tool such as R, SAS, Matlab, SPSS or Stata and you can seal the deal.
2. Get experience in general purpose programming. Experience in programming apps in general purpose languages like Java, C, Python or Scala can give you a competitive edge over candidates whose skill sets are confined to analytics. Wanted Analytics has noted a recent 337 percent increase in the number of job postings for programmers that required a background in data analytics. Those who understand and can communicate the connection between traditional app development and emerging analytics are in high demand.
3. Know Apache Hadoop. The core components of Apache Hadoop are essential to your big data toolbox of marketable qualifications. These include HDFS, MapReduce, Flume, Oozie, Hive, Pig, Hbase and YARN.
4. Be adept at Apache Spark. Spark is not as well known as Hadoop, but the rapid rise of its in-memory stack is increasingly used as a more efficient alternative to MapReduce-style analytics. It requires specialized technical expertise to program and run. Having this knowledge opens additional career opportunity doors.
5. Master NoSQL databases. Distributed scale-out NoSQL databases like MongoDB and Couchbase are taking over jobs previously handled by the likes of Oracle and IBM DB2. On the web and via mobile apps, NoSQL databases often are the source of data crunched in Hadoop. The two occupy opposite sides of a virtuous cycle.
6. Be an expert data miner. Data mining circa 2016 has reached a new level. One of the hottest big data fields is machine learning. If you can master the ability to build and train predictive analytic apps such as classification, recommendation and personalization systems, you can command top dollar in the marketplace.

Hand in hand with your statistical prowess, proficiency at SQL remains a key big data competency. SQL is experiencing a resurgence for the next generation of Hadoop-scale data warehouses, largely due to Cloudera’s Impala and similar initiatives.

Balance these skills with curiosity, communication and problem-solving strengths, and ironclad determination. The ability to creatively think your way through a situation and articulate the results is a major plus.

Are you looking to advance your big data career? The specialized recruiters at BrainWorks have the market intelligence, industry knowledge and connections you need. Read our related posts or contact us today to learn more.