5 Remarkable Data Careers — Which One Speaks to You the Most?
In the ever-changing world of information overload, there will always be a need for people that know how to organize data, store data, make data-driven decisions, and solve problems with data. If you have been thinking about where your role is in the data world, this article might be the guide to help you with that.
DATA
What is the first thing that comes to your mind when you come across the word — Data? Do you think of numbers, facts, or figures? Data is much more than that. The selfies stored on your devices that you love so much are a form of data, data is that song that you can't just stop listening to on Spotify or Apple Music, The article you are currently reading is DATA!
To put it simply, Data are units of information that are either numeric, sounds, words, images, they can be bytes and bits stored within the memory of electronic devices, facts stored in a person’s mind, etc. Data is everywhere and it is telling you (Refer to the meme below, Avenger)
Data Careers
The first thing I want you to keep in mind is that no matter the field of data you are in or choose, the primary thing you will be doing is solving problems so get ready to be a Data Superhero(I have appointed you). So what I would be doing is moving through the data process so you can find where you fit.
- Data Architect — A data architect creates the design plan for data management so that the databases can be easily incorporated, centralized, and protected with the best security measures. They organize data at a micro and macro level. They also ensure that the data engineers have the best tools and systems to work with. They are contemporary data modelers.
Skills & Talents
- Data warehousing solutions
- In-depth knowledge of database architecture
- Data Modeling
- Systems development
- Extraction, Transformation, Load(ETL), spreadsheet, and BI tools
Mindset
Inquiring Avenger with a love for data architecture design patterns
Language/ Fighting Strengths
- Spark
- SQL(Structured Query Language)
- Hive
- XML
- Pig Latin
- Data Engineer — Perform batch processing or real-time processing on gathered and stored data. Data engineers are also responsible for building and maintaining data pipelines that create a robust and interconnected data ecosystem within an organization, making information accessible for data scientists. Data engineers often focus on larger datasets and are tasked with optimizing the infrastructure surrounding different data analytics processes. For example, a data engineer might focus on the process of capturing data to make an acquisition pipeline more efficient. They may also need to upgrade a database infrastructure for faster queries. They are software engineers by trade.
Skills & Talents
- Database Systems(SQL & NO SQL Based)
- Data Modeling & ETL tools
- Data Apis
- Data warehousing solutions
Mindset
All-Purpose Avenger
Language/ Fighting Strengths
- SQL
- Hive
- Pig
- R
- Matlab
- SAS
- SPSS
- Python
- Java
- Ruby
- C++
- Perl
- Data Analyst — They analyze the company and industry data to find value and opportunities. Data analysts can be found in every industry, and job titles can vary. Some roles will have industry-specific names like “healthcare data analyst.” “Business analyst”, “intelligence analyst”, and similarly-named roles often share a lot with data analyst roles. One of the most important skills of a data analyst is optimization. This is because they have to create and modify algorithms that can be used to cull information from some of the biggest databases without corrupting the data. Unlike data scientists, they’re typically not expected to be proficient in machine learning. They are data detectives.
Skills & Talents
- Spreadsheet tools(e.g Excel)
- Database systems(SQL and NO SQL based)
- Communication and Visualization(e.g Tableau or Power BI)
- Maths and Statistics(A sprinkle of)
Mindset
Intuitive data junkie with high “figure-it-out” quotient
Language/ Fighting Strengths
- HTML
- SQL
- R
- Python
- Javascript
- Data Scientist — They find, clean, and organize data for companies. Data scientists will need to be able to analyze large amounts of complex raw and processed information to find patterns that will benefit an organization, help drive strategic business decisions and communicate actionable insights. Compared to data analysts, data scientists are much more technical.
Skills & Talents
- Distributed Computing
- Predictive Modelling
- Storytelling and visualizing
- Maths, Stats and Machine Learning
Mindset
Curious Data Wizard(I mean Merlin level type of wizard)
Language/ Fighting Strengths
- Pig
- SQL
- R
- Python
- Spark
- SAS
- Hive
- Matlab
- Machine Learning Engineer/Scientist — Machine learning engineers create data funnels and deliver software solutions. They typically need strong statistics and programming skills, as well as a knowledge of software engineering. In addition to designing and building machine learning systems, they are also responsible for running tests and experiments to monitor the performance and functionality of such systems. Machine learning engineers are also expected to perform A/B testing, build data pipelines, and implement common machine learning algorithms such as classification, clustering, etc. Machine Learning Scientists research new data approaches and algorithms to be used in adaptive systems including supervised, unsupervised, and deep learning techniques. Machine learning scientists often go by titles like Research Scientist or Research Engineer. They are historic leaders of data.
Skills & Talents
- Statistical theories and methodology
- Data Mining & Machine Learning
- Distributed Computing(Hadoop)
- Database systems(SQL and NO SQL based)
- Cloud tools
Mindset
Logical and enthusiastic Stats genius
Language/ Fighting Strengths
- Pig
- SQL
- R
- Python
- Spark
- SAS
- Hive
- Matlab
- SPSS
- Stata
- Perl
There are also MLOps engineers that specialize in combining Machine Learning, DevOps, and Data Engineering to deploy and maintain ML systems in production reliably. You might find yourself doing all the data roles because you are the go-to person for data in your organization, this is mostly dependent on the size of the data team in your organization. Don’t forget to lean towards roles where your strengths lie. Thank you for reading. If you have any question or comment, Kindly drop in the comment section.
