Data Engineer Roles and Responsibilities

What Does a Data Engineer Do?

Data engineers lay down the foundation of a database and its architecture. They assess a wide range of requirements and apply relevant database techniques to create a robust architecture. Afterward, the data engineer begins the implementation process and develops the database from scratch. After periodic intervals, they also carry out testing to identify any bugs or performance issues. A data engineer is tasked with maintaining the database and ensuring that it works smoothly without causing any disruption. When a database stops working, it brings a halt to the associated IT infrastructure. The expertise of a data engineer is especially needed to manage large-scale processing systems where performance and scalability issues need continuous maintenance. 

Data engineers can also support the data science team by constructing dataset procedures that can help with data mining, modeling, and production. In this way, their participation is crucial in enhancing the quality of data.

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Data Engineer Job Description

A data engineer is responsible for collecting, managing, and converting raw data into information that can be interpreted by data scientists and business analysts. Data accessibility is their ultimate goal, which is to enable organizations to utilize data for performance evaluation and optimization.

Data Engineer Roles and Responsibilities

Here is the list of roles and responsibilities, Data Engineers are expected to perform:

1. Work on Data Architecture

They use a systematic approach to plan, create, and maintain data architectures while also keeping it aligned with business requirements. 

2. Collect Data

Before initiating any work on the database, they have to obtain data from the right sources. After formulating a set of dataset processes, data engineers store optimized data. 

3. Conduct Research

Data engineers conduct research in the industry to address any issues that can arise while tackling a business problem. 

4. Improve Skills

Data engineers don’t rely on theoretical database concepts alone. They must have the knowledge and prowess to work in any development environment regardless of their programming language. Similarly, they must keep themselves up-to-date with machine learning and its algorithms like the random forest, decision tree, k-means, and others. 

They are proficient in analytics tools like Tableau, Knime, and Apache Spark. They use these tools to generate valuable business insights for all types of industries. For instance, data engineers can make a difference in the health industry and identify patterns in patient behavior to improve diagnosis and treatment. Similarly, law enforcement engineers can observe changes in crime rates. 

5. Create Models and Identify Patterns

Data engineers use a descriptive data model for data aggregation to extract historical insights. They also make predictive models where they apply forecasting techniques to learn about the future with actionable insights. Likewise, they utilize a prescriptive model, allowing users to take advantage of recommendations for different outcomes. A considerable chunk of a data engineer’s time is spent on identifying hidden patterns from stored data. 

6. Automate Tasks

Data engineers dive into data and pinpoint tasks where manual participation can be eliminated with automation.

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Skills Required to Become a Data Engineer

Here is the list of 7 Essential Data Engineer Skills:

1. SQL

SQL serves as the fundamental skill-set for data engineers. You cannot manage an RDBMS (relational database management system) without mastering SQL. To do this, you will need to go through an extensive list of queries. Learning SQL is not just about memorizing a query. You must learn how to issue optimized queries.

2. Data Warehousing

Get a grasp of building and working with a data warehouse; it is an essential skill. Data warehousing assists data engineers to aggregate unstructured data, collected from multiple sources. It is then compared and assessed to improve the efficiency of business operations.

3. Data Architecture

Data engineers must have the required knowledge to build complex database systems for businesses. It is associated with those operations that are used to tackle data in motion, data at rest, datasets, and the relationship between data-dependent processes and applications.

4. Coding

To link your database and work with all types of applications – web, mobile, desktop, IoT – you must improve your programming skills. For this purpose, learn an enterprise language like Java or C#. The former is useful in open source tech stacks, while the latter can help you with data engineering in a Microsoft-based stack. However, the most necessary ones are Python and R. An advanced level of Python knowledge is beneficial in a variety of data-related operations. 

5. Operating System

You need to become well-versed in operating systems like UNIX, Linux, Solaris, and Windows. 

6. Apache Hadoop-Based Analytics

Apache Hadoop is an open-source platform that is used to compute distributed processing and storage against datasets. They assist in a wide range of operations, such as data processing, access, storage, governance, security, and operations. With Hadoop, HBase, and MapReduce, you can further your skill sets. 

7. Machine Learning

Machine learning is mostly linked to data science. However, if you can have some idea of how data can be used for statistical analysis and data modeling, it will serve you well during your job as a data engineer. 

How Data Engineers Bring Value to Organizations?

Data engineers extract and acquire data from different sources, including the database – it can be SQL Server, Oracle DB, MySQL, Excel, or any other data storing or processing software. Afterward, they apply algorithms on this data and make it useful so it can assist different departments like marketing, sales, finance, and others to introduce more productivity in their work. 

Data engineers are entrusted with supervising the analytics in an organization. Data engineers equip your data with velocity. Businesses find it hard to make real-time decisions and accurately estimate metrics like fraud, churn, and customer retention. For instance, data engineers can help an e-commerce business learn which of their products will have more demand in the future. Similarly, it can allow them to target different buyer personas and deliver more personalized experiences to their customers.

As the world moves towards big data, data engineer certification can manage and leverage it to produce accurate predictions. By providing well-governed data pipelines, data engineers can improve machine learning and data models.

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How to Become a Data Engineer?

Becoming a data engineer requires a combination of education, technical skills, and experience. Here are some steps you can take to become a data engineer:

• Education

Most data engineering roles require a bachelor's degree in computer science, software engineering, or a related field. A degree in mathematics or statistics can also be helpful. You can also consider pursuing a master's degree in data science or a related field to gain additional knowledge and expertise.

• Technical Skills

Data engineers need to be proficient in programming languages like Python, Java, and SQL. They must also be familiar with big data technologies like Hadoop, Spark, and Kafka. Experience with cloud computing platforms like AWS, Azure, or Google Cloud Platform is also essential.

• Build a Portfolio of Data Engineering Projects

Gain hands-on experience working on data engineering projects. You can start with open-source projects or participate in hackathons and coding competitions. This will help you to build a portfolio of projects to showcase your skills and experience to potential employers.

• Learn Database Management

As a data engineer, you will need to be skilled in database management, data modeling, and data warehousing. Learn about different database systems, such as SQL and NoSQL databases.

• Keep Learning

The field of data engineering is rapidly evolving, so it's essential to stay up-to-date with the latest trends and technologies. Attend conferences, webinars, and workshops to learn new skills and network with other professionals in the field.

• Obtain Certifications

Consider obtaining certifications in data engineering, such as AWS Certified Big Data - Specialty, Google Cloud Professional Data Engineer, or Microsoft Certified: Azure Data Engineer Associate. This will help you to demonstrate your expertise to potential employers.

• Build a Network

Build a network of professionals in data engineering. Join online communities, attend meetups, and connect with other professionals on LinkedIn. This can help you to learn about new opportunities and stay up-to-date with the latest trends and technologies.

If you want to get hired for the role of a data engineer, enroll yourself for a Bachelor’s degree in Computer Science, Mathematics, or any other IT-related course of study. Certifications can provide further icing on the cake. This job requires a lot of understanding regarding theoretical aspects. 

You should have knowledge about database systems and data warehousing. Similarly, you should know how to perform a comparative analysis of data stores. Get your head around relational and non-relational database designs. This means having proficiency in both SQL and NoSQL domains. 

During your studies, experiment with personal projects and solve problems. Start from small projects and utilize different concepts one-by-one. Gradually, take part in open source projects to polish your skills.

Data Engineer Career Path

The career path of a data engineer can vary depending on the individual's skills, interests, and experience. Here is a general career path that a data engineer might follow:

• Entry-level Data Engineer

An entry-level data engineer typically has a bachelor's degree in computer science or a related field and some experience with programming languages, databases, and big data technologies. They work on data engineering projects under the guidance of more experienced data engineers.

• Junior Data Engineer

A junior data engineer has gained some experience in data engineering and has developed expertise in one or more programming languages, databases, and big data technologies. They may work on more complex projects and have more responsibility for designing and implementing data solutions.

• Senior Data Engineer

A senior data engineer has several years of experience in data engineering and has developed expertise in multiple programming languages, databases, and big data technologies. They may lead projects and teams of data engineers, and design and implement complex data solutions.

• Lead Data Engineer

A lead data engineer has extensive experience in data engineering and has demonstrated leadership skills. They may oversee a team of data engineers and have responsibility for designing and implementing data solutions across an organization.

• Data Architect

A data architect is responsible for designing and implementing data architectures that support an organization's business goals. They work closely with business stakeholders and data engineers to ensure data solutions are scalable, reliable, and secure.

• Data Infrastructure Manager

A data infrastructure manager is responsible for managing an organization's data infrastructure, including databases, data warehouses, and big data technologies. They oversee teams of data engineers and work closely with other IT departments to ensure that data solutions are integrated with other systems.

• Chief Data Officer

A chief data officer is a senior executive responsible for managing an organization's data strategy and ensuring that data is used effectively to support business goals. They oversee data engineering, data analytics, and data governance functions and work closely with other executives to ensure that data is a strategic asset for the organization.

Data Engineer Salary

According to Glassdoor, the average base salary for a data engineer in India is around ₹8,50,000 per year. However, this can vary based on several factors, such as location, years of experience, and the organization's size.

For instance, in metropolitan cities like Bangalore, Mumbai, and Delhi, a data engineer with 1-3 years of experience can expect an average base salary of around ₹7,00,000 - ₹9,00,000 per year, while someone with 4-6 years of experience can expect around ₹12,00,000 - ₹16,00,000 per year.

In smaller cities or towns, the average base salary for a data engineer can be lower than the metropolitan cities. Additionally, the salary can vary based on the individual's skills, certifications, and the specific tools and technologies they are proficient in. 

The demand for data engineering skills is high in India, and the salary for data engineers is expected to grow in the coming years.

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Data Scientist vs Data Engineer

Here is a detailed comparative analysis of data scientists and data engineers:

Skills:

Data scientists must have strong analytical skills, including statistical analysis, data visualization, and machine learning techniques. They also need to have a good understanding of programming languages like Python, R, and SQL. On the other hand, data engineers need expertise in database technologies, ETL (extract, transform, load) processes, and data warehousing. They should also be proficient in programming languages like Java, Scala, or Python.

Responsibilities:

Data scientists are responsible for analyzing data, identifying patterns and trends, and developing predictive models to help organizations make data-driven decisions. They also need to be able to communicate their findings to stakeholders and work collaboratively with other teams. On the other hand, data engineers are responsible for designing and building data pipelines, data warehouses, and data lakes. They need to ensure that data is easily accessible, reliable, and secure.

Tools and Technologies:

Data scientists use tools like Jupyter Notebooks, Python libraries like NumPy, Pandas, Scikit-Learn, and visualization libraries like Matplotlib and Seaborn. They also use machine learning frameworks like TensorFlow and PyTorch. Data engineers use tools like Apache Spark, Hadoop, and Apache Kafka. They also use cloud-based platforms like AWS, Google Cloud Platform, or Microsoft Azure.

Education and Training:

Data scientists typically have a background in statistics, computer science, or mathematics and often have a graduate degree in a related field. They must also continually update their skills to stay current with emerging technologies and techniques. Data engineers typically have a degree in computer science, software engineering, or a related field and often have experience in database technologies and data warehousing.

Career Growth:

Both data scientists and data engineers are in high demand, and there are many opportunities for career growth. Data scientists can progress to become senior data scientists, machine learning engineers, or data science managers. Data engineers can become senior data engineers, data architects, or data engineering managers.

Data Engineer vs Data Analyst

Data engineering and data analysis are two distinct fields in data science, with different skills and responsibilities. Here's a comparative analysis of data engineer and data analyst roles:

Job Responsibilities:

Data Engineers are responsible for designing, building, and maintaining the infrastructure that supports data storage, processing, and retrieval. They work with large data sets and develop data pipelines that move data from source systems to data warehouses, data lakes, and other data storage and processing systems. They also develop and maintain data APIs, ETL processes, and data integration systems.

On the other hand, data analysts are responsible for analyzing data to derive insights and support business decision-making. They work with data to identify patterns, trends, and relationships and present findings to stakeholders in an understandable and actionable way. They also develop dashboards, reports, and visualizations to communicate findings to non-technical audiences.

Required Skills:

Data Engineers require strong programming skills, particularly in languages such as Python, Java, Scala, and SQL. They should also understand database systems, distributed computing systems, and big data technologies such as Hadoop, Spark, and Kafka. They should also be familiar with cloud platforms such as AWS, Google Cloud, and Azure.

On the other hand, data analysts require strong analytical and data visualization skills. They should be proficient in SQL, Excel, Tableau, or other BI tools. They should also have a good understanding of statistical analysis and modeling techniques, as well as business acumen.

Educational Requirements:

Data Engineers typically have a degree in Computer Science, Software Engineering, or a related field. They should also have experience with database systems, distributed computing, and big data technologies. They may also have relevant certifications in cloud platforms or data engineering tools.

Data Analysts may have a degree in Mathematics, Statistics, Economics, or a related field. They should also have experience in data analysis, statistical modeling, and data visualization. They may also have relevant certifications in BI tools or data analysis techniques.

Getting Certified For Your Data Engineering Career Path 

In the past few years, the demand for data engineer roles has risen astronomically. Organizations are actively looking for data engineers to address their data woes. This skillset is high in demand, and it is far from being oversaturated like other fields. Those who pick up these skills have an opportunity to make high salaries. For this purpose, the right certification can turn out to be quite useful.

If you want to improve your data engineering skillset and stand out from the competition, consider getting Caltech Post Graduate Program in Data Engineering from Simplilearn. 

Frequently Asked Questions (FAQs)

1. What does a Data Engineer do?

A data engineer is a professional who designs, builds, maintains, and manages the data architecture and infrastructure necessary for storing, processing, and analyzing large amounts of data. 

2. Who does a data engineer work with?

A data engineer works with various professionals within an organization. Here are some of the key stakeholders that a data engineer may collaborate with:

• Data Scientists
• Business Analysts
• Database Administrators
• Software Engineers
• Data Architects
• Project Managers

3. What skills does a good data engineer require?

A good data engineer should possess a range of technical and soft skills. Here are some of the key skills that are important for a data engineer:

• Programming Skills: A data engineer should be proficient in programming languages such as Python, SQL, and Java, commonly used in data engineering.
• Data Modeling: A good data engineer should be able to design, implement and maintain data models that can support the organization's data storage and analysis needs.
• Database Management: A data engineer should have a solid understanding of database management systems (DBMS) such as MySQL, Oracle, or MongoDB.
• ETL (Extract, Transform, Load): A data engineer should be able to design and implement ETL pipelines that can extract data from various sources, transform it, and load it into the data warehouse or data lake.
• Big Data Technologies: A data engineer should be familiar with big data technologies like Hadoop, Spark, and Kafka.
• Cloud Computing: With the increasing adoption of cloud-based solutions, a data engineer should be familiar with cloud technologies such as AWS, Azure, or Google Cloud.
• Collaboration and Communication Skills: A data engineer should be able to work collaboratively with cross-functional teams, communicate effectively with technical and non-technical stakeholders, and document their work.
• Problem-Solving and Analytical Skills: A good data engineer should be able to identify problems, troubleshoot issues, and analyze data to derive insights and make informed decisions.

4. Do data engineers code?

Data engineers are responsible for coding and building the infrastructure, enabling an organization to store, process, and analyze large amounts of data. They typically use programming languages such as Python, SQL, Java, or Scala to develop data pipelines and ETL (Extract, Transform, Load) processes that extract data from various sources, transform it to the desired format, and load it into a data warehouse or data lake.

5. Is data engineering a good career?

Yes, data engineering is a rapidly growing field with a high demand for skilled professionals. With the increasing adoption of big data technologies, cloud computing, and data analytics, the need for data engineers is expected to continue to grow.

Data engineering offers a promising career path with competitive salaries, excellent job prospects, and opportunities for growth and advancement. According to Glassdoor, the national average salary for a data engineer in the United States is around $1,14, 000 per year.

However, becoming a successful data engineer requires combining technical, problem-solving, and collaboration skills. Keeping up with the latest technologies and industry trends is also essential for success in this field. If you have a passion for working with data and enjoy solving complex problems, data engineering can be a rewarding and fulfilling career choice.