Climbing the Analytics Career Ladder

Working in data analytics opens a great many doors but as with anything those opportunities will only materialise if you lay the right foundations. Whether you’ve come to an analytics career from an unrelated route or you’re slowly working your way up the proverbial analytics career ladder, there are two critical things you need to make it to base camp one, and those are the right skills and hands-on experience.

Starting out as a Junior Analyst or Data Analyst – depending on existing experience – you’ll cut your teeth on data extraction, cleaning said data and analysing it in order to present useful findings to the business and guide their decisions. Your likely starting data analytics job will be a junior role, however certain transferable skills and experience involving problem solving and the interpretation of data may qualify you for something a little more advanced. This ultimately rests with the organisation hiring you.

Once you have around 1-4 years’ practical experience that will have seen you performing root cause analyses on internal and external databases, manipulating, processing and extracting value from large, disconnected datasets, developing databases, leading and working with cross-departmental teams including senior personnel, and getting up close and personal with the relevant industry standards; you can start looking at career progression.

At this point you’ll likely be applying for mid-level roles within the analytics bracket, so anything from Data Analyst to Data Engineer, Supply Chain Analyst, Systems Analyst and so on are ripe for the picking. The pace at which you progress along your data analytics career path will largely depend on the size of the company you’re working for and whether you’re making moves within your own organisation or sowing your professional seeds in pastures new. It’s important to remember though, that different organisations will operate differently, perhaps assign duties a little differently under a similar role heading and expect different things of its analysts.

Certain certifications are typically required of analytics professionals, these include the CIPM (Certified Information Privacy Manager), CFA (Chartered Financial Analyst), CAIA (Chartered Alternative Investment Analyst Association), and SQL (Structured Query Language) which demonstrate to an employer your technical proficiency within analytics. As well as relational database knowledge, which is particularly important for those wanting to progress to Senior Data Analyst within the financial services sector.

At anywhere between 4-8 years into your career, roles like Analytics Manager or Senior Data Analyst will begin to become tangible. These senior management roles will see you move away from the frontline expectations to focus your skills more towards data strategy. The more experience you have the more trust an organisation will have in you taking ownership of their data processes as well as managing your own team of analysts.

With 6-7 years’ experience under your belt, you can also pursue a consultant role which will give you more variety in your professional life, whether you opt to work for a consulting firm or contract out on a self-employed basis.

Aside from the level of seniority and what that looks like in terms of analyst job description, you also need to think carefully about where your interests lie, and which industry you want to work in. Data analyst jobs span everything from insurance and digital marketing to financial services, healthcare and social media. Taking the specialist data analyst careers route will rely on you having honed your expertise within one of those specific fields.

You may choose to abandon the analyst path altogether and switch gears to a data scientist job, which will see you focusing more on analysing and using data to make predictions that will streamline the way the business functions. Data scientist jobs require a more technical skillset so expect to become well-acquainted with machine learning, data modelling, algorithms and languages such as Python and R. Data scientist careers open up opportunities to progress to positions like Machine Learning Engineer, Senior Data Scientist or in some cases Chief Data Officer.

Whether you become a master business strategist, head of the department or a specialist in a particular area, all roads lead from a willingness to learn the basics, get your hands “dirty” and invest in the tools, resources and qualifications needed to further your analytics career.