Data Scientist, Data Engineer & Other Data Careers, Explained

The data-related career landscape can be confusing, not only to newcomers, but also to those who have spent time working within the field.

Get in where you fit in. Focusing on newcomers, however, I find from requests that I receive from those interested in join the data field in some capacity that there is often (and rightly) a general lack of understanding of what it is one needs to know in order to decide where it is that they fit in. In this article, we will have a look at five distinct data career archetypes, and hopefully provide some advice on how to get one’s feet wet in this vast, convoluted field.

We will focus solely on industry roles, as opposed to those in research, as not to add an additional layer of complication. We will also omit executive level positions such as Chief Data Officer and the like, mostly because if you are at the point in your career that this role is an option for you, you probably don’t need the information in this article.

So here are 5 data career archetypes, replete with descriptions and information on what makes them distinct from one another.

Data Architect

The data architect focuses on engineering and managing data stores and the data that reside within them.

The data architect is concerned with managing data and engineering the infrastructure which stores and supports this data. There is generally little to no data analysis needing to take place in such a role (beyond data store analysis for performance tuning), and the use of languages such as Python and R is likely not necessary. An expert level knowledge of relational and non-relational databases, however, will undoubtedly be necessary for such a role. Selecting data stores for the appropriate types of data being stored, as well as transforming and loading the data, will be necessary. Databases, data warehouses, and data lakes; these are among the storage landscapes that will be in the data architect’s wheelhouse. This role is likely the one which will have the greatest understanding of and closest relationship with hardware, primarily that related to storage, and will probably have the best understanding of cloud computing architectures of anyone else in this article as well.

SQL and other data query languages — such as Jaql, Hive, Pig, etc. — will be invaluable, and will likely be some of the main tools of an ongoing data architect’s daily work after a data infrastructure has been designed and implemented. Verifying the consistency of this data as well as optimizing access to it are also important tasks for this role. A data architect will have the know-how to maintain appropriate data access rights, ensure the infrastructure’s stability, and guarantee the availability of the housed data.

This is differentiated from the data engineer role by focus: while a data engineer is concerned with building and maintaining data pipelines (see below), the data architect is focused on the data itself. There may be overlap between the 2 roles, however: ETL; any task which could transform or move data, especially from one store to another; starting data on a journey down a pipeline.

Like other roles in this article, you might not necessarily see a “data architect” role advertised as such, and might instead see related job titles, such as:

• Database Administrator
• Spark Administrator
• Big Data Administrator
• Database Engineer
• Data Manager

Data Engineer

The data engineer focuses on engineering and managing the infrastructure which supports the data and data pipelines.

What is the data infrastructure? It’s the collection of software and storage solutions that allow for the retrieval of data from a data store, the processing of data in some specified manner (or series of manners), the movement of data between tasks (as well as the tasks themselves), as data is on its way to analysis or modeling, as well as the tasks which come after this analysis or modeling. It’s the pathway that the data takes as it moves along its journey from its home to its ultimate location of usefulness, and beyond. The data engineer is certainly familiar with DataOps and its integration into the data lifecycle.

From where does the data infrastructure come? Well, it needs to be designed and implemented, and the data engineer does this. If the data architect is the automobile mechanic, keeping the car running optimally, then data engineering can be thought of as designing the roadway and service centers that the automobile requires to both get around and to make the changes needed to continue on the next section of its journey. The pair of these roles are crucial to both the functioning and movement of your automobile, and are of equal importance when you are driving from point A to point B.

Truth be told, some the technologies and skills required for data engineering and data management are similar; however, the practitioners of these disciplines use and understand these concepts at different levels. The data engineer may have a foundational knowledge of securing data access in a relational database, while the data architect has expert level knowledge; the data architect may have some understanding of the transformation process that an organization requires its stored data to undergo prior to a data scientist performing modeling with that data, while a data engineer knows this transformation process intimately. These roles speak their own languages, but these languages are more or less mutually intelligible.

#data analyst #data engineer #data engineering #data management #data science

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Data Engineer, Data Scientists & Other Data Careers, Explained.

This week, take part in our survey and let us know where you recently applied Data Science, Analytics and Machine Learning. Also: Data Scientist, Data Engineer & Other Data Careers, Explained; A Guide On How To Become A Data Scientist (Step By Step Approach); A checklist to track your Data Science progress; How to Determine if Your Machine Learning Model is Overtrained; Differentiable Programming from Scratch; and much, much more.

Our new KDnuggets Top Blogs Reward Program will pay to the authors of top blogs - check details here. Reposts accepted, but we love original submissions, rewarded at 3 times the rate of reposts.

#kdnuggets 2021 issues #analytics #careers #data engineer #data engineering #data science #data scientist #poll #survey

50 Data Science Jobs That Opened Just Last Week

Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.

In this article, we list down 50 latest job openings in data science that opened just last week.

(The jobs are sorted according to the years of experience r

1| Data Scientist at IBM

**Location: **Bangalore

Skills Required: Real-time anomaly detection solutions, NLP, text analytics, log analysis, cloud migration, AI planning, etc.

Apply here.

2| Associate Data Scientist at PayPal

**Location: **Chennai

Skills Required: Data mining experience in Python, R, H2O and/or SAS, cross-functional, highly complex data science projects, SQL or SQL-like tools, among others.

Apply here.

3| Data Scientist at Citrix

Location: Bangalore

Skills Required: Data modelling, database architecture, database design, database programming such as SQL, Python, etc., forecasting algorithms, cloud platforms, designing and developing ETL and ELT processes, etc.

Apply here.

4| Data Scientist at PayPal

**Location: **Bangalore

Skills Required: SQL and querying relational databases, statistical programming language (SAS, R, Python), data visualisation tool (Tableau, Qlikview), project management, etc.

Apply here.

5| Data Science at Accenture

**Location: **Bibinagar, Telangana

Skills Required: Data science frameworks Jupyter notebook, AWS Sagemaker, querying databases and using statistical computer languages: R, Python, SLQ, statistical and data mining techniques, distributed data/computing tools such as Map/Reduce, Flume, Drill, Hadoop, Hive, Spark, Gurobi, MySQL, among others.

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#careers #data science #data science career #data science jobs #data science news #data scientist #data scientists #data scientists india

Data Scientist, Data Engineer & Other Data Careers, Explained

The data-related career landscape can be confusing, not only to newcomers, but also to those who have spent time working within the field.

Get in where you fit in. Focusing on newcomers, however, I find from requests that I receive from those interested in join the data field in some capacity that there is often (and rightly) a general lack of understanding of what it is one needs to know in order to decide where it is that they fit in. In this article, we will have a look at five distinct data career archetypes, and hopefully provide some advice on how to get one’s feet wet in this vast, convoluted field.

We will focus solely on industry roles, as opposed to those in research, as not to add an additional layer of complication. We will also omit executive level positions such as Chief Data Officer and the like, mostly because if you are at the point in your career that this role is an option for you, you probably don’t need the information in this article.

So here are 5 data career archetypes, replete with descriptions and information on what makes them distinct from one another.

Data Architect

The data architect focuses on engineering and managing data stores and the data that reside within them.

The data architect is concerned with managing data and engineering the infrastructure which stores and supports this data. There is generally little to no data analysis needing to take place in such a role (beyond data store analysis for performance tuning), and the use of languages such as Python and R is likely not necessary. An expert level knowledge of relational and non-relational databases, however, will undoubtedly be necessary for such a role. Selecting data stores for the appropriate types of data being stored, as well as transforming and loading the data, will be necessary. Databases, data warehouses, and data lakes; these are among the storage landscapes that will be in the data architect’s wheelhouse. This role is likely the one which will have the greatest understanding of and closest relationship with hardware, primarily that related to storage, and will probably have the best understanding of cloud computing architectures of anyone else in this article as well.

SQL and other data query languages — such as Jaql, Hive, Pig, etc. — will be invaluable, and will likely be some of the main tools of an ongoing data architect’s daily work after a data infrastructure has been designed and implemented. Verifying the consistency of this data as well as optimizing access to it are also important tasks for this role. A data architect will have the know-how to maintain appropriate data access rights, ensure the infrastructure’s stability, and guarantee the availability of the housed data.

This is differentiated from the data engineer role by focus: while a data engineer is concerned with building and maintaining data pipelines (see below), the data architect is focused on the data itself. There may be overlap between the 2 roles, however: ETL; any task which could transform or move data, especially from one store to another; starting data on a journey down a pipeline.

Like other roles in this article, you might not necessarily see a “data architect” role advertised as such, and might instead see related job titles, such as:

• Database Administrator
• Spark Administrator
• Big Data Administrator
• Database Engineer
• Data Manager

Data Engineer

The data engineer focuses on engineering and managing the infrastructure which supports the data and data pipelines.

What is the data infrastructure? It’s the collection of software and storage solutions that allow for the retrieval of data from a data store, the processing of data in some specified manner (or series of manners), the movement of data between tasks (as well as the tasks themselves), as data is on its way to analysis or modeling, as well as the tasks which come after this analysis or modeling. It’s the pathway that the data takes as it moves along its journey from its home to its ultimate location of usefulness, and beyond. The data engineer is certainly familiar with DataOps and its integration into the data lifecycle.

From where does the data infrastructure come? Well, it needs to be designed and implemented, and the data engineer does this. If the data architect is the automobile mechanic, keeping the car running optimally, then data engineering can be thought of as designing the roadway and service centers that the automobile requires to both get around and to make the changes needed to continue on the next section of its journey. The pair of these roles are crucial to both the functioning and movement of your automobile, and are of equal importance when you are driving from point A to point B.

Truth be told, some the technologies and skills required for data engineering and data management are similar; however, the practitioners of these disciplines use and understand these concepts at different levels. The data engineer may have a foundational knowledge of securing data access in a relational database, while the data architect has expert level knowledge; the data architect may have some understanding of the transformation process that an organization requires its stored data to undergo prior to a data scientist performing modeling with that data, while a data engineer knows this transformation process intimately. These roles speak their own languages, but these languages are more or less mutually intelligible.

#data analyst #data engineer #data engineering #data management #data science

Data Analyst, Data Scientist, and Data Engineer — What's the Difference?

Data engineer, data analyst, and data scientist — these are job titles you’ll often hear mentioned together when people are talking about the fast-growing field of data science.

There are plenty of other job titles in data science and data analytics too. But here, we’re going to talk about:

1. 1
2. The “big three” roles (data analyst, data scientist, and data engineer)
3. 2
4. How they differ from each other
5. 3
6. Which role is best for you

Although precisely how these roles are defined can vary from company to company, there are big differences between what you might be doing each day as a data analyst, data scientist, or data engineer.

We’re going to dig into each of these specific roles in more depth.

What is a Data Analyst?

Data analysts deliver value to their companies by taking data, using it to answer questions, and communicating the results to help make business decisions.

Common tasks done by data analysts include data cleaning, performing analysis and creating data visualizations.

Depending on the industry, the data analyst could go by a different title (e.g. Business Analyst, Business Intelligence Analyst, Operations Analyst, Database Analyst). Regardless of title, the data analyst is a generalist who can fit into many roles and teams to help others make better data-driven decisions.

What do data analysts do?

The data analyst has the potential to turn a traditional business into a data-driven one.  Their core responsibility is to help others track progress and optimize their focus.

How can a marketer use analytics data to help launch their next campaign? How can a sales representative better identify which demographics to target? How can a CEO better understand the underlying reasons behind recent company growth? These are all questions that the data analyst provides the answer to by performing analysis and presenting the results.

While often data analyst positions are “entry level” jobs in the wider field of data, not all analysts are junior level. As effective communicators with mastery over technical tools, data analysts are critical for companies that have segregated technical and business teams.

An effective data analyst will take the guesswork out of business decisions and help the entire organization thrive. The data analyst must be an effective bridge between different teams by analyzing new data, combining different reports, and translating the outcomes. In turn, this is what allows the organization to maintain an accurate pulse check on its growth.

#career #career tips #data analyst #data engineer #data science #data scientist

5 Indian Companies Recruiting Data Scientists In Large Numbers

According to a recent study on analytics and data science jobs, the number of vacancies for data science-related jobs in India has increased by 53 per cent, since India eased the lockdown restrictions. Moreover, India’s share of open data science jobs in the world has seen a steep rise from 7.2 per cent in January to 9.8 per cent in August.

Here is a list of 5 such companies, in no particular order, in India that are currently recruiting Data Scientists in bulk.

#careers #data science #data science career #data science jobs #data science recruitment #data scientist #data scientist jobs