What is a Data Analytics Engineer? Meaning, Missions & Skills
By Brian Laleye · November 14, 2022 · 7 min read
Let’s discover in this article what a Data Analytics Engineer and more precisely the role of this function, his mission and the essential skills he should have.
What is a Data Analytics Engineer?
The data analytics engineer comprises team members responsible for efficient products and data models. This team helps document the dataset representations with the well-tested form used by the rest of the companies for answering the questions.
They work for moving and transforming the data from the source that would help in analyzing, visualizing, and working upon by the data analyst.
Data analytics engineers also possess technical skills for applying software engineering practices, for example, Version Control and CI/CD. Moreover, it helps in communicating with stakeholders to use these tools effectively.
The data analytics engineers’ datasets help the end-users examine all kinds of information in the given data. The data analytics engineers use all their business strategy and technical data knowledge to translate complex data, which can be easily visualized with various data models. The fundamental role of data analytics engineers is the collaboration of data analysts and engineers to provide the data visualizations of its patterns along with commutation with the stakeholders, coworkers, and end-users.
Analytics engineers are capable of handling the data along with managing and sorting data. They ensure that the data is ingested, scheduled, transformed, and ready for analytics which can be used by all who require it. Most analytics engineers are the orchestrators of the modern data stack. Moreover, they decide and apply for ETL/ELT tools.
Role and Mission of a data analytics engineer
The data analytics engineer is the one who is responsible for the implementation of the data warehouse along with its management to ingest data by selecting the best tools for the ingestion of data from multiple warehouses into this warehouse. After which, the frame of the model of the data can be further used by the analysts along with scheduled tests for making your model simple.
- Data Warehouse Management
Engineers are highly responsible for ingesting the data into the data warehouse and maintaining the datasets. They lie the priority to be notified if any issue arises in the pipeline, which would further fix it.
- Data Modelling
This is the method that involves the process of generating a visual representation of the data between the various information locations and systems. Analytics engineers are involved in charging and modeling the raw data into datasets that help the analytics in the company. The related datasets work as a central source of truth, making it easier for business analytics and various other stakeholders to view and manage the data in the database.
- Data Orchestration
The data analytics engineers create pipelines and workflows to move the data from one point to another. Moreover, it coordinates, verifies, and stores the data for analysis. Data analytics engineers are highly skilled in data orchestration and automation.
- Setting best practices
Data analytics engineers help enable other team members, such as data scientists and analysts, to perform more effectively. They do this by sharing tips for writing SQL or doing tasks on datasets to include new metrics or dimensions. Moreover, they also train by giving information on how to apply best practices for software engineering. In short, this approach is known as data ops. It is a new methodology in which data analytics, data engineering, and DevOps are integrated. Some of the other few practices that are optimizable are version control, data unit testing the CI/CD(Continuous Integration and Continuous Delivery) practice.
As they are team members in any business, thus they collaborate with other team members effectively to gather the business requirements and suggest better analytics outcomes and data models.
The analytical data engineers collect all information, write code, design data model tasks, maintain data documentation, etc. Moreover, they also perform communication tasks with various stakeholders. However, analytical data engineers connect business acumen with the technical expertise needed in business growth along with business strategy and data development.
Essential Skills For Data Analytics Engineer
There are tremendous sets of skills that data analytics engineers should acquire to become successful data analytics engineers in this competitive world. Some of the skills are discussed below:
- SQL and DBT Prowess
Data analytics engineers must acquire sufficient knowledge of working with SQL to write most of the transformations in data models. Thus, SQL becomes the superior skills one must master to become a successful data analytics engineer. Some of the SQL-related problems include creating a logic for the data transformation, writing queries, and developing optimized data models.
- Programming languages
It is essential to learn advanced programming languages such as Python and R to become a data analytics engineer for handling various tasks related to data orchestration. Most data pipeline tools also require coding with programming languages like Python. If you know how to code in Python, it will be easy for you to write your pipeline as a data analytics engineer.
- Recent data stack tools
The data analytics engineer must learn various data stack tools, which involve doing tasks such as ingestion, transformation, deployment, and warehousing tools. If you need more comprehensive knowledge in this at the initial stage, try to gather at least basic knowledge about these tools.
- Knowledge of BI Tools
A data analyst engineer needs to be highly skilled with various tools for building data pipelines. Some of these tools are data warehouses like Amazon Redshift and Google BigQuery.Moreover, the ETS tools include AWS Glue, Talend, etc. Along with these, some of the business intelligence tools it has are Tableau, Looker, etc.
The data analytics engineer is excellent at modeling data to provide accurate data; thus, everyone, whether it is in the team or not, can understand and can utilize data accordingly. Their role involves gathering data, transforming, testing, and finally documenting it. They possess a decent to advanced level of programming skills, communication skills, etc.
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