The Key Roles and Responsibilities of a Data Engineer

Businesses produce a lot of data. Everything from customer feedback to sales performance and stock price influences how a company operates. But understanding what stories the data tells isn’t always easy or intuitive, which is why many businesses rely on data engineering.

What is Data Engineering?

Data engineering is the process of designing and building systems that let people collect and analyse raw data from multiple sources and formats. These systems empower people to find practical applications of the data, which businesses can use to thrive.

All the different structured data that the world produces needs to be delivered to other components in the software structure or visualized and interpreted by business analysts and data scientists. However, this delivery is not a simple task.

Therefore, the delivery of this information is a complex process and, if it is not carried out properly, it can be very problematic, especially for medium and large-scale companies. This is where data engineers come into play.

Data engineering is a very broad discipline that comes with multiple titles. In many organizations, it may not even have a specific title. Because of this, it’s probably best to first identify the goals of data engineering and then discuss what kind of work brings about the desired outcomes.

The goal of data engineering is to provide organized, consistent data flow to enable data-driven work, such as:

• Training machine learning models
• Doing exploratory data analysis
• Populating fields in an application with outside data

This data flow can be achieved in any number of ways, and the specific tool sets, techniques, and skills required will vary widely across teams, organizations, and desired outcomes. However, a common pattern is the data pipeline.

The data can come from any source:

• Internet of Things devices
• Vehicle telemetry
• Real estate data feeds
• Normal user activity on a web application

Depending on the nature of these sources, the incoming data will be processed in real-time streams or at some regular cadence in batches.

The pipeline that the data runs through is the responsibility of the data engineer. Data engineering teams are responsible for the design, construction, maintenance, extension, and often, the infrastructure that supports data pipelines. They may also be responsible for the incoming data or, more often, the data model and how that data is finally stored.

What Are the Key Skills of a Data Engineer?

Data engineers need to be literate in programming languages used for statistical modelling and analysis, data warehousing solutions, and building data pipelines, as well as possess a strong foundation in software engineering.

While data engineers’ job specs will vary across different industries, most hiring managers focus on:

• Database systems like SQL and NoSQL
• Data warehousing solutions
• ETL tools
• Machine learning
• Data APIs
• Python, Java, and Scala programming languages
• Understanding the basics of distributed systems
• Knowledge of algorithms and data structures

RESPONSIBILITIES AND ROLES OF A DATA ENGINEER

Potential data engineering candidates will be expected to:

• Create and maintain optimal data pipeline architecture
• Assemble large, complex data sets that meet business requirements
• Identify, design, and implement internal process improvements
• Optimize data delivery and re-design infrastructure for greater scalability
• Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and AWS technologies
• Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency, and other key business performance metrics
• Work with internal and external stakeholders to assist with data-related technical issues and support data infrastructure needs

How does Data Engineering benefit companies

Nowadays, Big Data and advanced analytics are crucial tools for online businesses like retailers and travel applications. Improving data analytics can potentially increase a company’s operating margins by over 60%. On the other hand, industries like the healthcare sector can reduce 8% of their costs by improving their data storage systems.

Data engineering is indisputably important in every business and for digital automation. From future analysis to present day-to-day operations, data engineering is the key to the longer shelf life of your business entity. While you might be able to record the data incoming on a daily basis, it is of little to no use if it is not comprehensible, moreover, it must also be coherent, a task best done by data engineers. To be able to use data, it must be readable, and it is the very innate function of data engineering. In fact, accessible and actionable business intelligence has been proven to facilitate up to 5x faster decision-making. 

Data Engineering Solutions Help Modern Businesses Handle IT Systems. Business data engineers help your data scientists store the collected data in an accessible format. Moreover, this data can be structured, unstructured, or semi-structured. And it might be too large to be stored without advanced compression technologies. 

Modern Businesses Need the APIs of Data Engineering Services. API means Application Programming Interface, and developing APIs is time-consuming. So, agile businesses outsource the task of API development to competent business data engineers. 

Future Scope of a Data Engineer

Data engineering is soaring high and there are newer trends that are coming up. Here is a peep into the possible futuristic trends that data engineers would enjoy, in their upcoming ventures:

• There will be data engineering support for every team
• Real-time infrastructure will become standardized
• Data engineers will be involved in the DevOps methodology
• Product-based data engineering will rise further
• Remote working for data engineers will increase
• Increase in self-service analytics through modern-day tools

In terms of the number of jobs posted compared to other data-related jobs, data engineering ranks number one with almost a 50% growth rate. In recent years, the demand for data engineers has surpassed the supply.

The average salary for a data engineer in the U.S. is as high as $116K. Top companies pay even higher. This makes data engineering one of the highest-paid data-related jobs today. 

3 Common Data Engineer Job Roles

Data Engineer’s job profiles vary widely between companies. The scope of these roles depends largely on the size of the company, the maturity of its data operations, and the volume of data collected.

• Small companies: A data engineer on a small team may be responsible for every step of data flow, from configuring data sources to managing analytical tools. In other words, they would architect, build and manage databases, data pipelines, and data warehouses—basically doing the work of a full-stack data scientist. 
• Mid-size companies: In a mid-sized company, data engineers work side by side with data scientists to build whatever custom tools they need to accomplish certain big data analytics goals. They oversee data integration tools that connect data sources to a data warehouse. These pipelines either simply transfer information from one place to another or carry out more specific tasks.
• Large companies: In a large enterprise with highly complex data needs, a typical data engineer job spec requires data engineers to focus on setting up and populating analytics databases, tuning them for fast analysis, and creating table schemas. This involves ETL (Extract, Transfer, Load) work, which refers to how data is taken (extracted) from a source, and converted (transformed) into a format that can be analysed and stored (loaded) into a data warehouse.

To assist in arming students with the deep understanding and unique skills to help mould this ever-changing industry, Hero Vired has a Certificate Program in Data Engineering that will provide you with in-depth knowledge of Python, SQL & NoSQL, Airflow, Kafka, Spark, Scala, Hive, AWS 53, Azure, and MongoDB. 

The program focuses on how to Extract, Transform and Load (ETL) live and pre-stored data in the most efficient manner. It helps you gain an understanding of the relevant storage infrastructure required to build, deploy and test application layers for efficient data loading.