Database administrators and database engineers work to create, maintain, and improve systems and frameworks to guarantee data remains secure, sorted out, and available. If you analyse, the two positions require remarkable programming aptitudes and understanding of on software systems. Database administrators are liable for the upkeep and day by day function of a database, while database engineers work to refine existing databases or create new ones. Here we look into the two job roles in a little detail:

Database Admin

A DBA’s usual job is to ascertain everything is working smoothly with things like performance tuning and monitoring, data migrations of third party systems, performing backups, checking performance, and load balancing, and everything to do with databases. On the other hand, the truth is that the DBA matches the role of the data engineer in mid-size and smaller organisations. It depends on the kind of organisation a person works in, the size of the technical team, etc.

The position entails running regular diagnostic tests to ensure data is not corrupt and combing for bugs or glitches within the system. Safely storing and backing-up data in case of a system failure or memory loss and creating plans for addressing large-scale errors are also essential responsibilities of a DBA.

Database admins work across a broad range of platforms, including Oracle, SQL Server, MySQL, PostgreSQL and more. Their work needs streamlined, efficient workflow, task automation, granular coverage for each database platform, advanced SQL optimisation, performance testing, real-time performance diagnostics, and sensitive data discovery and protection.

DBA offer includes working with the software development team to implement schema changes, doing migrations, writing Bash and SQL scripts for the database. DBAs are generally focused on operational aspects of relational systems — keeping RDBMS performing, tuning queries, managing schema evolution, backup/restore operations.

Data Engineers 

Data engineers are usually considered to have a solid knowledge of the information within a system they work with but do not have an understanding of the database engine or the infrastructure that supports it.

Data engineers are focused on manipulating data in a software engineering capacity. Some of that data might live in relational systems, but it’s increasingly moving towards NoSQL systems and data lakes. They normalise databases and ascertains the structure of the data meets the requirements of the applications that are accessing the information. 

So, data engineers work with a lot of streaming systems, and more likely to be working in a big data ecosystem involving Hadoop, Spark, Kafka, Kinesis, cloud storage, Cassandra, elastic search, and other NoSql storage.  

You will need to learn the rest very quickly on the job, like specific tools (Spark/Hive/Airflow etc.), particular databases (Redshift/Snowflake) query tuning, monitoring, data validation, cloud infrastructure, DWH data modelling.

Job Functions of Database Engineers vs Administrators

The specific obligations regarding database administrators and engineers can contrast dependent on the organization or industry objectives. The ranges of skills requirement for these two positions may also overlap. Involvement in large-scale data systems, expertise in programming languages such as SQL, Java, and Python, and database configuration are major requirements for these two positions.

What separates a database admin from a database engineer is the focal point of their duty. An admin is generally worried about the ordinary capacity of the database all in all. Their undertaking is to ensure the database runs easily and safely. Database engineers center more around the effectiveness of explicit procedures used to gathering and move information inside the database.

Final Thoughts

These days DBAs frequently spend their time maintaining legacy database systems running, until a time when they may be migrated to newer systems. As a data engineer, a person should not be responsible for provisioning and maintaining the data infrastructure, and so it should not matter whether it is on-premise or cloud. 

According to experts, the cloud has made obsolete a lot of the skills which DBAs have. They say that database administration role may tend to decrease because of higher-level databases which require less administration, and mainly because of cloud solutions that may not require much administration. Whereas data engineering will keep growing with the cloud, maybe not indefinitely either, but surely has a transparent future.

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Artificial Intelligence continues to evolve through businesses across diverse industries, opening opportunities for organisations to operate and drive values to customers. From managing global supply chains to optimising delivery routes, the technology is augmenting its dominance in today’s digital world. This is why, AI is in high demand as businesses, be it small or large, are seeking to garner a competitive edge.

According to reports, demand for AI jobs continues to increase, and with more open jobs than qualified candidates to fill them, many AI-related roles rule high salaries. Similar to every country in the world, India is also racing to gain AI’s potential benefits that could be game-changing. In the country, AI is gaining rapid traction as large tech giants are looking at the country for new opportunities for their business growth.

Here are the top AI job titles with the highest pay in India.

Data Scientist

Data Scientists’ responsibilities revolve around Identifying valuable data sources and automating collection processes; Undertaking preprocessing of structured and unstructured data; Analyzing large amounts of information to discover trends and patterns, and more. They work closely with business stakeholders to understand their goals and determine how data can be used to accomplish those goals.

The average salary of a Data Scientist in India is INR 10,15,385 (US$13,332.26).

Data Engineer

Data engineers are the ones responsible for finding trends in data sets and developing algorithms to help make raw data more useful to the enterprise. They are often accountable for building algorithms to assist in providing easier access to raw data.

The average base pay of a Data Engineer is INR 8,25,676 (US$10,848.10).

Data Analyst

Data Analysts deliver value to their companies by taking insights about specific topics and then interprets, analyzes, and presents findings in comprehensive reports. They acquire data from primary or secondary data sources and maintain databases.

The average salary of a Data Analyst is INR 5,19,956 (US$6,831.42).

Algorithm Engineer

An Algorithm Engineer is responsible for developing an algorithm system that records all operations and can be maintained by the team, and managing design, development, and deployment of scalable, high volume and real-time system. He/she also need to research on algorithm improvements and implement data processing.

The average salary of an Algorithm Engineer in India is INR 7,21,192 (US$9,475.35)

Computer Vision Engineer

Computer vision engineers often apply computer vision research that is based on a large sum of data to solve real-world problems. They spend much of their time researching and implementing machine learning primitives and computer vision for their client companies. A Computer vision engineer has a significant amount of experience with a variety of systems, such as image recognition, machine learning and segmentation.

The national average salary of a Computer Vision Engineer is INR 4,17,549 (US$5,485.95).

Machine Learning Engineer

A Machine Learning Engineer is proficient in using data to training models, which are then used to automate processes such as image classification, speech recognition, and market forecasting. He/she works close to that of a data scientist as both roles perform with a large amount of information, and require excellent data management skills and the ability for complex modeling on dynamic data sets.

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Demand for data scientists and engineers has, for the past couple of years, been off the charts. The number of openings for machine learning and data engineers posted on recruiting web sites continues to grow by double digits annually, and those working in the field have been commanding ever-higher salaries.

Joining the ranks of these desperately sought after techies takes serious coding chops, definitely expertise in Python, along with familiarity with other languages. That combination—of job openings for data engineers along with the dominance of Python, means Python regularly makes the charts of most in-demand coding languages.

So anyone contemplating a future in data science or machine learning needs to build up software engineering skills, right?

Wrong, says Ryohei Fujimaki, founder and CEO of dotData. Fujimaki has, for nearly a decade, been working to use AI to automate much of the job of the data scientist.

We can, he says, “eliminate the skill barrier. Traditionally, the job of building a machine learning model can only be done by people who know SQL and Python and statistics. Our system automates the entire process, enabling less experienced people to implement machine learning projects.”

DotData—which is currently offering its tools as a cloud-based service—came out of NEC. Fujimaki, then a research fellow at the company, started thinking about automating machine learning in 2011 as a way to make the 100 or so data scientists on his research team more productive. He got sidetracked for a few years, focused on commercializing an algorithm designed to make machine learning transparent, but in 2015 returned to the machine learning project.

“A typical use case for machine learning in the business world is prediction,” he said, “predicting demand of a product to optimize inventory, or predicting the failure of a sensor in a factory to allow preventive maintenance, or scoring a list of possible customers.”

“The first step in developing a machine learning model for prediction is feature engineering—looking at historical patterns and coming up with hypotheses,” he says. Feature engineering generally requires a team of people with a multitude of skill sets—data scientists, SQL experts, analysts, and domain experts. Typically, only after this team comes up with a set of hypotheses does machine learning step in, combining all those hypotheses to figure out how to best weigh them to come up with accurate predictions.

In dotData’s system, AI takes over that first step, coming up and testing its own hypotheses from a set of historical data.

So, he says, “you don’t need domain experts or data scientists, and as a subproduct AI can explore many more hypotheses than human experts—millions instead of hundreds in a limited time window.”

Fujimaki’s group at NEC in 2016 let Japan’s Sumitomo Mitsui Banking Corp. (SMBC) test a prototype against a team using traditional data science tools. “Their team took three months, our process took a day, and our results were better,” he says. NEC spun off the group in early 2018, remaining as a shareholder. Right now DotData has about 70 employees, about 70 percent of those are engineers and data scientists, along with a few dozen customers, Fujimaki says.

“In the near future,” Fujimaki says, “80 percent of machine learning projects can be fully automated. That will free up the most skilled, computer-science-PhD-type of data scientists, to focus on the other 20 percent.”

Demand for data scientists overall won’t drop from what it is today, Fujimaki predicts, though the double-digit growth may slow. The job, however, will become more focused. “Data scientists today are expected to be superman, good at too many things—statistics, and machine learning, and software engineering.”

And a new role is likely to emerge, he predicts. “Call it the business data scientist, or the citizen data scientist. They aren’t machine learning people, they are more business oriented. They know what predictions they need, and how to use those predictions in their business. It will be useful for them to have basic knowledge of statistics, and to understand data structures, but they won’t need deep mathematical understanding or knowledge of programming languages.

“We can’t eliminate the skill barrier, but we can significantly lower it. And here will be many more potential people who will be able to do this.”

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New Delhi: Great Learning, India’s leading EdTech company for working professionals has launched a new program, the Data Science Fellows Program, aimed at providing unmatched learning and career outcomes to students and recent alumni (graduated less than 3 years ago) from the top 150 engineering colleges in the country. This competitive, seven-month program will arm the learners with skills that are perfectly calibrated with premium jobs in Data Science and Analytics. With this program, Great Learning has made a strong commitment to invest in the learners’ future, and is offering a “PAY LATER” option modelled on Income Sharing Agreements to students selected for the program. All qualified and interested candidates will have the opportunity to go through the rigorous evaluation process, including a 3-week preparatory bootcamp course called “DS Foundation Track”.

The Data Science Fellows program builds on Great Learning’s pedigree in Analytics and Machine Learning, where they have consistently been ranked the best in India. The program will help learners transition to highly competitive roles such as Data Scientist, Business Analyst, Data Analyst, Data Engineer, and Machine Learning Engineer. Over the course of 7 months, learners hone these skills through quality learning material, personalized mentorship and support, rigorous assessments, and hands-on application of their learnings on industry-relevant problems and case studies.

While the demand for professionals in this field is high, organizations face a shortage of skilled professionals which is slowing down their business. This new program, introduced by Great Learning bridges this gap in applicable skills through multiple mini-projects, capstone projects and hackathons. Learners will also develop proficiency with tools and technologies such as Python, Tableau and SQL. While the Data Science Fellows Program is intense and comprehensive, it is also designed to allow participants to learn with minimal disruption to their professional and academic commitments.

Speaking on the launch of this new Program, Mr. Hari Krishnan Nair, Co-founder, Great Learning said, “Data Science is expected to add significant growth and value to the world’s economy and it will impact organizations across sectors. Data is a big asset for organizations today, which are increasingly relying on the application of Data Science and Machine Learning to gain a competitive advantage. To help them make the most of these technologies, these organizations are looking for high-quality talent with ready-to-use Data Science skills. To bridge this gap, and to help young professionals boost their career in Data Science, we have collaborated with Great Lakes Executive Learning to create this program. Our endeavour is to find the best talent from the top engineering colleges, equip them with the skills that companies are looking for, and connect them to corporates seeking such talent.”

In 2020, India is expected to be one of the top markets for Data Science in the world, and there has been a 60 percent rise in demand for data science experts in the industry over the last 2 years. According to NASSCOM, there are presently over 1 lakh job opportunities for professionals skilled in data science, and these jobs are among the best paying jobs in the industry today.

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Hailed as the sexiest career of the 21st century, Data Science has emerged as one of the most sought-after and competitive fields of today. As the accuracy and efficiency of data-driven decision-making increases, the demand for data science experts has seen exponential growth.

While new jobs and roles are cropping up every day, existing jobs are also evolving through the use of more data analysis, requiring professionals to add more advanced skills to their portfolios.

Therefore, for a recent graduate or a mid-career professional seeking to build a career in Data Science, it is imperative to have a thorough understanding of the job market to know what skills they need in order to successfully do so. Let’s take a look at some of the most trending roles in Data Science across industries.

Analyst

There are a plethora of profiles that analysts can find themselves working in, including Data Analyst, Business Analyst, Marketing Analyst, Systems Analyst, Operations Analyst, Quantitative Analyst, and Business Intelligence Analyst.

These are entry-level roles at the lower end of a company’s hierarchy, essentially involving churning out insights from various raw data sets to make strategic recommendations for the business.

A data analyst typically deals with more data, and therefore more sophisticated data analysis techniques, than a business, operations or marketing analyst. A business analyst, though, may climb up the ladder to become a specialised Business Intelligence (BI) analyst.

Key skills required: R, Python, or C/C++ with SQL-based database management specialisations.

Data Engineer

A qualified data engineer designs the system responsible for making raw data application-ready for data scientists and analysts. A data engineer engages with the database available and performs data processing.

The focus is more on data crunching and interactions with the database to establish the system infrastructure. This infrastructure is, in turn, used by an analyst to derive meaning out of the pile of data.

Key skills required: A background in software engineering with fluency in programming languages such as SQL, Java, R, Matlab, Python, SAS, SPSS, Ruby.

Architect

As an architect in the field of data science, there are several roles to explore – data, data warehouse, applications, infrastructure, and enterprise. An architect ensures healthy interactions, integrity and security of the domain he is responsible for.

For instance, a data architect facilitates database protection, maintenance, and efficient information retrieval.

Similarly, an applications architect ensures healthy interactions between various applications running in a software system along with monitoring their real-time behaviour.

Infrastructure and enterprise architect roles, being more managerial and less technical, ensure optimal functioning of the company’s infrastructure and safe-keeping strategies and resources.

Key skills required: SQL, Hive, Spark.

Data Scientist

Data Scientist is the most specialised and sought-after job in this area of work. As a data scientist, one must be able to decipher an ocean of data, find hidden trends and patterns in it and then communicate it in an easy-to-understand manner.

This specialised role needs an amalgamation of machine learning, data mining, analytical and statistical skills.

Key skills required: Programming ability and fluency in a sophisticated platform (such as Matlab, R, JuPyter) must be second nature. Business or domain knowledge is a distinct advantage.

Machine Learning Engineer/Scientist

A machine learning engineer is responsible for creating models, researching new data-science based approaches and employing statistical algorithms and data to generate key insights that help in delivering business solutions.

Key skills required: A background in programming, statistics, data modelling, machine learning algorithms, software engineering.

If you are a fresh graduate or still at a nascent stage in your career, an internship is often a fantastic opportunity to work with an organisation’s data scientists, machine learning engineers and analysts, and learn from their rich experience while working on a hands-on project.

Additionally, one can also explore freelancing and entrepreneurial opportunities, after having gained solid skills in the domain.

Almost all companies require a data scientist in today’s digital world but don’t necessarily have the resources to hire a full-time specialist. This opens up spaces for freelancers that can lead to establishing an independent consultancy.

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