How Data Analytics Engineering Is Strengthening Modern Day Businesses

Earlier, when ETL (Extract, Transform, and Load) systems functioned on on-premise databases, an organization’s data team consisted of two key positions of Database Developers and Business Analysts. Then, the role of Data Engineers (evolved from Database Developers) and the demand of Data Scientists surfaced with the emergence of deep learning and big data.

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Nitin Nijhawan, CDO
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Nitin Nijhawan, CDOicon

Now, the accelerated processing power of data warehouses and the adoption of cloud-based warehouse services have eliminated the need for the data to be in a transformed state before being loaded onto the data warehouse. As a result, the ELT (Extract, Load, and Transform) paradigm has occurred, creating a new role – Data Analytics Engineering.

This article will help you gather an in-depth understanding of this new role, its responsibilities, and how the expertise of analytics engineering can help your business achieve its goals. 

What Is Data Analytics Engineering?

The role of data analytics engineering emerged with the adoption of the ELT approach in the data integration landscape. This entailed that data now sits in the warehouse before being transformed into actionable insights useful for businesses. This gap between data extraction and transformation created room for accuracy problems and potential data breaches. 

Hence, to defend against these vulnerabilities, many companies have put compliance protocols in place that require the data to be encrypted, cleansed, and treated for safety before landing in a warehouse. Today, more sophisticated ETLT solutions have cropped up to help businesses meet the said objective. 

The new and evolving role of an analytics engineer is to model the organized, well-tested datasets that enable the company to find answers to pesky questions. These sets are built and documented in a way that communicates effectively with business users and gives consistent and reliable answers to all data consumers.

Analytics engineering for business is a merger of business analytics and engineering. An analytics engineer has an unmatched level of technical expertise in leveraging the best practices of software engineering, such as version control and CI/CD, combined with a strong business sense. 

The Differences of Traditional Data Services and Modern-day Analytics Engineering Services

Traditionally, a data team consisted of three prominent roles: data engineers, data analysts, and data scientists. Let’s take a closer look at their work:

Data Engineers build and manage infrastructure by extracting data from databases and SaaS tools. The data is then transformed and loaded into a data warehouse and made accessible across the organization.

Data Analysts visualize the data in the form of dashboards and reports with the goal of enabling stakeholders to find the answers. They also perform exploratory analysis.

Data Scientists extract value from data using statistics and machine learning to solve optimization problems, run A/B experiments, build prediction models, and do more.

These roles have existed since the data itself came into being. However, a series of innovations have brought the role of an analytics engineer to the fore. Firstly, the evolution of the data transformation process introduced innovations in data tooling. Cloud-driven warehouses brought down the speed and cost of data processing. And business intelligence (BI) tools enabled companies of all sizes to utilize big data to improve performance.

The declining role of a data engineer is a prominent feature of modern-day data teams. New innovations have changed the way a data structure is built and managed. Also, data analysts now work with BI tools in addition to their traditional tasks, whereas data scientists are responsible for cleaning data for testing.

Data Analytics Engineering Experts: Key Roles and Responsibilities

  1. Building Reusable Data Assets

An analytics engineer’s primary responsibility is to set up data assets from the infrastructure and environment established by the Data Engineers. To this end, analytics engineers generate, maintain and deploy transformation codes to build such data assets using the best software engineering practices. 

  1. Ensuring Access to Data Assets

Analytics engineers ensure that data assets are accessible to everyone who needs it. They put data into well-defined, documented, well-tested, and code-reviewed datasets. The documentation is a meticulous step wherein the lineage and other features of the dataset are traced to its source, making it easy for data customers to search through such details.

  1. Maintaining the Data Assets

On-going monitoring and updating of the data assets are critical to ensure that data consumers get fresh, valuable, and error-free information. To this goal, analytics engineers define and establish standards to communicate the relevancy of their data assets and provide data engineers with an escalation process when problems arise.

  1. Setting up the Quality Standards

Analytics engineers are also tasked with defining quality standards for data engineers to follow. This includes writing scripts and cleansing algorithms to refine data and defining criteria for the accepted sources of data. These data quality metrics dictate the value offered to data customers and also show the effectiveness of quality improvement efforts.

  1. Collaborating with the Traditional Data Team

Analytics Engineers work amongst data engineers, data scientists, and business analysts. Since the latter two are the primary data customers, they are most likely to know the key purposes the data should help them achieve. The analytics engineer’s role is to understand this exact data asset requirement, sometimes by coaching them to accurately express their needs. This collaboration results in the creation of a format for documenting future asset requirements.

How do Data Analytics Engineering Services Help Modern-day Businesses in Achieving Their Goals?

  • Increased Productivity of the Data Team

The role of analytics engineering has made data teams more efficient and is driving business innovation using in-warehouse data transformation. Since analytics engineers understand the value of data in decision making, they can conjure up the possible use cases of raw data and accordingly lead its transformation.

  • Improved Data Quality

Analytic engineers have a crucial role in building actionable datasets. This is the result of their cross-functional skill set, meaning they understand both the science behind data and the business goals. This results in superior quality data in comparison to the data generated through a segmented workflow of a traditional data team.

  • Implementing Specific Technology

Analytic engineers are well-versed with modern tools, such as DBT, that require the person to be comfortable with both SQL and the workflows of software engineering. This intelligence of an analytics engineer allows the organization to adopt new data strategies and directions and get maximum ROI out of their modern tools.

VLink and the Future Of Analytics Engineering

Despite being a relatively new domain, the future of data analytics engineering for businesses is quite exciting. As the need for well-modeled, accurate data and its areas of application rises, organizations will want to hire additional data talent, i.e., analytics engineers. This modern data team will increase efficiency in workflows, elevate the amount and quality of insights obtained from data, and add a competitive edge to your business. 

To embrace and evolve with this fast-approaching future of analytics engineering, VLink can help you adopt smart analytics engineering toolsets and best practices. Learn how our best data analytics solutions can help you build a single data table that has answers to your entire set of business questions.


The expertise of a skilled analytics engineer can prove to be an invaluable asset to an organization in building a truly data-driven culture – something the traditional data teams are still far from achieving. VLink is your trusted big data consulting services provider that can help you benefit from the data engineering tools, best practices, and the multi-faceted expertise of analytics engineering.
We offer a whole spectrum of data engineering consulting services, including ETL, reporting and instrumentation tools, predictive analytics, and other solutions. Our IT solutions facilitate strategic partnership and optimized value chain to meet industry-specific goals for organizations of all sizes. Contact us to schedule a consultation today.

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