Find Your Perfect Fit: A Quick Guide for Job Roles in the Data World
Data related positions are considered the hottest in the job market during the last couple of years. While everyone wants to join the party and enter this fascinating field, it is essential to first get an understanding. In this quick guide, I’ll do my best to dispel the confusion by crystalizing the essence of the different positions.
By Alon Mei-raz, VP Data & Insights, Bank Hapoalim
A couple of weeks ago, while taking my dog out for a walk, I stumbled upon my neighbor. Coming from a statistics background, she asked me about the different roles in the data world, trying to figure out her next career move. After I outlined the various responsibilities for each one of the job roles, I witnessed she was quite puzzled. "I had no idea this field is so fragmented", she said. And she’s not the only one.
Data related positions are considered the hottest in the job market during the last couple of years. The demand keeps growing rapidly, and it’s not expected to change anytime soon. While everyone wants to join the party and enter this fascinating field, it is essential to first get an understanding of the various R&Rs (Roles and Responsibilities). In this quick guide, I’ll do my best to dispel the confusion by crystalizing the essence of the different positions.
So let’s get started.
The main responsibility of a data analyst is to identify important business questions, then process and use data for enabling the organization to make more informed data-driven decisions.
This role requires a wide set of skills, from gathering large amounts of data and organizing it to reach insights. Data analysts must possess both analytical and technical capabilities, and are expected to be familiar with ETL tools, data visualization and languages / technologies such as: R, Python, SQL, SAS, etc.
While this role is not as technical as the other ones on the list, business analysts play an important role in the data world, as the link between the technical personas and the business side / management. They must have a deep understanding in their specific industry (e.g.: healthcare, insurance, finance) and the business processes.
Since business analysts are the intermediators to the business side and management, they need to be able to produce reports, have decent data visualization skills and obviously be top-notch communicators.
Data Engineers are the “builders” in the group. Some refer to them as the DevOps of the data sphere. I’ve seen different companies define this role quite differently, but in my view data engineers lay the groundwork that enables other roles, such as data scientists and data analysts to successfully do their jobs. In order to achieve that, the data engineers are trusted with the important responsibility of building and maintaining the big data eco-system for the organization, while making sure it’s robust and running smoothly.
Data engineers need to be pretty savvy about data systems, such as: Hadoop, Hive, MongoDB, MySQL, etc. They should also have hands-on experience with data streaming tools, ETL tools and data modelling.
Well, I initially wanted to leave this one to the end, since it’s obviously the most sought-after position out there - not only in the data world, but also generally in the tech community. Nevertheless, I do believe the collaboration between all the roles on this list significantly contributes to the success of an organization. That said, the reason I think it attracts so many professionals lies in the fact that data science, by definition, is the junction between three key areas: programming, statistics and business knowledge. It also involves a lot of creativity, since data scientists start from a business question and need to find the optimal path for answering it, using a variety of advanced techniques like predictive analysis. They are tuned towards conducting research for observations one wouldn’t have reached without deep analysis of data to the point of realizing patterns, linkages and behaviors of data, and then being able to realize how to utilize those to the benefit of the organization they are working for.
Data Scientists are expected to be experts in statistics and math, and of course in programming languages, such as: Python, R, Scala.
Machine Learning Engineer
Another in-demand role, which has some overlap with data engineering / data science.
Machine learning engineers are in charge of bridging the gap between the data scientist and technology that would facilitate delivering the benefits of a data scientist’s outcome to production or to the service of the organization. They do so by building data pipelines, moving models to production, exposing APIs, training the models and performing A/B testing.
ML engineers need to have in-depth knowledge of various machine learning libraries (e.g.: Tensorflow, NLTK), coding experience and strong knowledge in SQL, Rest APIs and other complementary technologies.
While most of the focus during the last couple years has shifted towards artificial intelligence, we must not forget the importance of business intelligence. Both AI and BI are key to the success and decision making in modern organizations.
BI developer usually assigned to develop and maintain BI interfaces: data visualization and dashboards, reporting and query tools. In terms of required skills, here are some useful ones for a BI developer: SQL, deep understanding of OLAP and ETL, and experience with BI systems: Power BI, Qlik Sense or others.
Database Administrator (DBA)
This role is the veteran on the list. The DBA has the critical role of setting up and maintaining the database. By being responsible for the health of the organization’s database, the DBA is basically in charge of one of the firm’s most valuable assets. The DBA’s activities include: manage access to the database (grant / revoke, etc), plan and archive backup routines (and recoveries), plan and execute installations & upgrades, monitor the database and optimize the performance of it.
DBAs need to obviously master the database they’re in charge of.
In a nutshell, the ETL developer is in charge of the process of transferring data from a source database to a target database, including monitor and test the performance of the process and fix it when needed. In large-scale systems, this process occurs very frequently, therefore is crucial.
ETL developers must have experience with the following: ETL tools (the popular ones are: Talend, Informatica, Datastage), SQL, scripting languages and modelling tools.
I consider this role and the one that follows to function as the glue of the team. The data architect is basically the technical glue, leading all architectural activities. That includes creating blueprints and design documents for specifying database flows and integration points, evaluation and approval of proper tools for the engineers to deploy and use. The data architect should also act as a “gate keeper”, making sure the organization’s data vision is enforced, obviously with the needed security measures.
The way I see it, the data architect must be a jack of all trades. This means having in-depth knowledge when it comes to data technologies and best practices and keeping up-to-date with the latest advances.
Data Product Owner
Data product owners are responsible for leading the data strategy of the organization and overseeing the product portfolio in terms of leveraging data and alignment to the vision.
First and foremost, a data product owner is, well, a product owner. In general, product owner defines the roadmap, collaborates with internal and external stakeholders to make sure it’s moving forward and function as the “glue of the project”. On top of all these activities, the data product owner is in charge of making sure the organization maximizes the value of data to achieve optimal business results. In some cases, this means influencing senior management by presenting the benefits of leveraging data, and also making sure it is widely enforced and embraced across the company.
Data talents are no longer exclusively head-hunted by tech companies. These days, most companies out there already understand the power of data and its importance to the growth of the organization. Keep in mind that companies might vary in their definitions and scope of the different roles described above.
As previously mentioned, while everyone has probably heard about the data scientist role, there are many more roles that comprise the data world. Each with its specific challenges and required skills. If you want to land a job in the data sphere, make sure you’re well familiar with the variety of roles and the differences between them, which sometimes may be subtle and even overlap.
So, what are you waiting for?
Bio: Alon Mei-raz is the VP Data & Insights at Israel’s leading bank, heading all data platforms and groups which supply insights to support over 2M customers. Alon has held various tech leadership and senior management positions at HPE, Sun Microsystems and other market-leading companies for over 15 years, with a vast experience in the mobility, machine learning, and chatbots fields.
- 5 Career Paths in Big Data and Data Science, Explained
- Advice for a Successful Data Science Career
- 4 Realistic Career Options for Data Scientists