In today’s Australian job market if you’ve got some Big Data experience, you’re mostly likely getting approached by recruiters and are probably spoilt for choice. If that’s the case, you don’t need to read on. This article is for the rest of you, who have heard about this ‘Big Data’ thing and are wondering how to get your foot in the door.
Given Contexti’s focus on the Big Data Analytics market in Australia, we’re fortunate to be aware of and in many cases involved in a broad range of Big Data Analytics related conversations, deals, projects, partnerships, hires, fires and events across Australia. The single biggest challenge we constantly hear about is the shortage of qualified and experienced ‘Big Data’ people.
While we don’t advise our customers to drop their standards in the quality of their hires, we do strongly warn against holding on to the belief that there is a magical unicorn big data guru out there. Instead we suggest organisations hire professionals with the right fundamentals (e.g. fit for culture & values, coachable, possess skills in certain technologies or analytics methods, etc) and implement a plan to develop them into capable Big Data Analytics practitioners.
Similarly we’ve found ourselves having conversations with a broad range of professionals, some who are just starting our their careers and are thinking about graduate roles while others with decades of experience who now want to transition into a career in the growing Big Data Analytics space.
Like everything else in business and life there are no silver bullets, but if you approach this in a strategic and tactical manner, you will massively improve the odds in your favour.
So here are five tips to help you land a big data job in Australia:
#1 Define your target role
While ‘data scientist’ sounds like an exciting role, it may not be the right entry point for you. You want to get into a role where you will learn and where you will also quickly add value by bringing something to the table. To do this, think about your ‘home ground advantage’, what skills, experience or connections do you already have and map it to the closest Big Data role in the an industry most suitable to you.
Some real-world examples we are aware of:
- Our own Damion Reeves at Contexti transitioned from being an experienced Database Administrator (DBA) with years of experience in infrastructure, Oracle and SQL to a Big Data Platform Engineer. While Hadoop and Spark were technologies he needed to learn, his underlying experience with Linux and UNIX, capabilities in shell scripting and knowledge of enterprise support and service protocols were immediate value-adds to Contexti and to our customers.
- Our client Sharmaine Salis Head of Data Architecture at Seven West Media transitioned from a traditional Business Intelligence / Data Warehouse solutions role into a Big Data / Cloud Architect role, leading one of the most successful big data projects in Australia which underpinned Seven’s Rio Olympics games coverage.
- One of our Hadoop & Spark training students, MingJian Tang currently a Cyber Security Data Scientist at the Commonwealth Bank of Australia (CBA), transitioned into this role from a statistics and data mining background.
- Broader in the field we’ve seen someone with solid Telco background move into a Big Data Strategy role for one of the Telcos trying to monetise their data assets.
So the take-aways are:
- There are many potential Big Data Analytics roles (Commercial Strategist, Platform Architect, Data Architect, Platform Engineer, Data Engineer, Analyst, Data Scientist, Project Manager, Quality Assurance, Sales, Business Development, Customer Success etc).
- No one-person will be qualified to do all the available roles in Big Data.
- Find your home ground advantage and target a role that gets you excited and one where you can add value quickly.
#2 Skill up
You will massively improve your chances in landing a role if you’ve invested in skilling yourself up. The one obvious benefit is the theoretical and in many cases the practical knowledge you will gain by attending formal training. The other not so obvious benefit is the network of relationships you will create with the instructor and other class participants. Depending on the role, your budget, time availability etc there are many courses to take advantage of. Here are some of the short to long training and certification programs we are aware of:
- Online – AWS Cloud Training with Linux Academy
- Spark & Hadoop Training & Certification with Cloudera (Delivered by Contexti)
- Introduction to R & Data Visualisation Training (Delivered by Contexti)
- Master of Data Science and Innovation, University of Technology Sydney (UTS)
An important factor in landing a new role is ‘who you know and who knows you’. Networking enables you to to build relationships, get known, learn something new and contribute. There are many meetup groups and networking events. Here are some of the ones we attend:
- Sydney Big Data Analytics
- Tech Talks Pivotal Labs (Sydney)
- Melbourne Big Data Analytics
- Canberra Data Scientists
- Brisbane AWS meetup
- Sydney AWS meetup
- Sydney Users of R Forum
- Web Analytics Wednesdays (Sydney)
#4 Be found
There are many ways to get your name out there and to be found. Speaking at events and meet ups, writing guest blogs posts, publishing your work in online forums (GitHub, Slideshare etc), getting active on Twitter and Quora. The simplest and most obvious one however is to put effort in your LinkedIn profile. After you consider your target role and your home ground advantages (existing skills, industry experience etc) as well as your training and up-skilling strategy, you should update your LinkedIn profile.
Your profile should be authentic. This means stating correctly what you have done, skills you possess and how much experience you actually have. Further an authentic profile should include objectives, aspirations and current activities you are undertaking to improve yourself giving the potential recruiter an idea of not only where you’ve been but a view of where you are headed.
A recent example was when I was doing a search on LinkedIn for anyone who had included “Data Science” in their profile. I came across a professional who had recently completed a data science course in addition to having a math and statistics major and hands-on actuarial work experience. His LinkedIn headline said ‘Aspiring Data Scientist’. The word ‘aspiring’ gave me an indication of where he was headed and what he was looking for yet it was authentic as he wasn’t claiming to be an experienced data scientist.
This approach can be applied to your LinkedIn headline and your summary where you can include your ‘elevator pitch’ of who you are, where you’ve been, what your great at and where you are heading.
#5 Look for early signals
To narrow down your targeting efforts and improve your odds, look for early signals that might lead you to a future job opportunity. Typically this will be keeping your eyes open on LinkedIn, subscribing to relevant industry news and blogs, reading mainstream business and technology news and being an active networker. Early signals you should keep your eyes on include: companies announcing changes in strategy, appointment of new leaders, new partnerships or vendors winning contracts.
For example in the last six months in Australia there have been a number of executive movements in the Chief Data Officer and Chief Digital Officer roles, this kind of appointment usually indicates a company is reprioritising ‘data’ as a strategic priority and is usually followed by a restructure and a recruitment drive. There have also been a number of public announcement of data deals and data partnerships as well vendors announcing contracts with new customers or publishing case studies of success stories with existing customers.
All of these are early signals that will give you hints on people, companies, technologies and deals to follow and target in order to land your next big data job.
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