In our last blog post, we took a closer look at GDPR, specifically from a security perspective. In this blog post, we change gears a little and revisit big data and analytics. Specifically, we look at career paths in big data and analytics. Whether you’re new to big data and analytics, or considering a career change, sidestep or even just curious as to what’s out there we’re sure you’ll find some new insights!
Why consider a career in Big Data and Analytics
Before we look at some career paths in big data and analytics, it’s worthwhile mentioning why you might want to consider a career in the industry!
Big Data and Analytics skills are among the highest in demand in tech at the moment. In fact, according to Forbes, the blend of skills that form big data and analytics expertise is wanted by as much as 75% of IoT providers!
Cross-industry and domain
With firms such as Oracle, IBM (Watson), Microsoft (Azure Data Services) and SAP investing billions into data management and analytics to help support multiple industries and problem domains, a career in big data analytics can offer you varied employment and opportunities to widen your skill set.
In 2017, LinkedIn found that 12 of the 20 most promising jobs in its social network were in tech. Out of these “three of the top five technical jobs are analytics-based.”. LinkedIn also went onto find out that “Analytics-related positions have a median salary significantly above the median income for all tech positions taken in aggregate.” A career in big data and analytics can be appealing to those wishing to improve their salary level!
Now that we’ve explored some of the reasons why you might want to consider a career in analytics, let’s explore what some of those roles actually look like:
Big Data and Analytics skills are among the highest in demand in tech at the moment. Click To Tweet
The business analyst is a multifaceted role. From a soft skills perspective, the business analyst can be responsible for interacting with the user community, and helping to elicit requirements that can be folded into product designs. From a big data and analytics perspective, the business analyst can be responsible for designing and building queries that let the business arrive at actionable insights using existing or combined datasets.
As a business analyst, you need to be comfortable interacting with both internal and external stakeholders, not to mention be technically proficient in terms of understanding complex reporting requirements and in terms of using querying languages to extract the data or insights businesses are looking for.
Professionals wishing to embark on a business analysts role in the context of big data and analytics, therefore, need to be comfortable with technologies such as:
- SQL & relational databases
- NoSQL databases
- Commercial reporting and dashboard package know-how
- Responsive ad-hoc reporting, and sound knowledge of tools for quickly adapting
- Data warehousing
Don’t worry if you don’t have all of the above skills as business analysts often have to tap into other skill sets or team members such as engineers or data scientists to help them arrive at the insights the business demands.
Average US Salary: $77,712
The data engineer role involves compliments the Data Management professional role and in terms of ensuring the system and database architecture is sound and fit for its purpose. That said, data engineers need to recommend ways to improve the reliability of data, its efficiency and the overall quality of the data.
Quite often, the data engineer connects disparate systems together, or undertake analysis to help identify new data sources in the enterprise that can help the business with the task at hand.
Data engineers use tools that can include some of the following:
There is certainly overlap in terms of skills from the Data Management Professional but both work
together to make sure the infrastructure and data is ready for the data scientist for further
massaging, cleansing and processing.
Average US Salary: $151,301
The data scientist has possibly been one of the hottest positions in tech. It’s often difficult to define exactly what the roles and responsibilities exactly are, but the data scientist is primarily focused on manipulating, interpreting and processing data to help complete the task at hand.
With the data scientist being such a generalist position, the skills and technologies the role demands can be varied. For example, maybe the data can only be accessed over a web service – the data scientists, therefore, might have to be proficient in writing code that connects to REST web services. Or maybe the data has been exported into gigabytes worth of CSV files, in which case the data scientist must have the skills to write SSIS packages or queries.
For the role of data scientist, some key skills can include, but are not limited to:
- Writing databases queries
- Excel (yes, Excel!)
- SQL Server, Oracle
- Data munging in R or Python
- Using non-relational, non-structured data and extracting it to a flat file, or even other formats
- Engineering features after initial exploratory analysis
Average US Salary: $139,840
Machine Learning Practitioner
Machine learning and artificial intelligence have been making headlines for a few years now. The background to machine learning can be found in statistics and analytics and the role of the machine learning practitioner or engineer is to leverage their analytic and statistical skills to use tools or build models that can processes data, at scale, and in some cases even predict the outcome of events given specific datasets.
Algorithms that machine learning experts use can be complex. Their biggest assets include statistics, mathematics and programming skills. These can be used to help them build complex models or tooling that helps businesses process big data to make critical or strategic decisions.
3 of the top 5 technical jobs are analytics-based Click To Tweet
Important skills and technologies for the machine learning practitioner tend to include:
- Algebra and calculus
- Programming skills: C++, Python, R
- Solid understanding of the range of machine learning algorithms such as Bayesian theorem
That said, providers such as Microsoft with their Azure and Cognitive Services platform have effectively democratized machine learning for businesses and developers around the world making it simpler for business to deploy complex machine learning algorithms – no Ph.D. required!
Average US Salary: $114,826
In this blog post, we’ve looked at some career paths in Big Data and Analytics. We’ve explored why this area of tech is growing and how some of the most lucrative salaries in the industry are in the big data and analytics vertical.
We’ve looked at how roles such as the Data Management Professional are accessible for existing tech professionals and how you don’t need a Ph.D. in linear algebra to embark on a career in some of these professions. Advancements in cloud technology are making it easier than ever to surface actionable insights in the enterprise.
Here at Growth Acceleration Partners, we have extensive expertise in many verticals. Our nearshore business model can keep costs down whilst maintaining the same level of quality and professionalism you’d experience from a domestic team.
Our Centers of Engineering Excellence in Latin America focus on combining business acumen with development expertise to help your business. We can provide your organization with resources in the following areas:
- Software development for cloud and mobile applications
- Data analytics and data science
- Information systems
- Machine learning and artificial intelligence
- Predictive modeling
- QA and QA Automation