Analytics Engineers

Hire Analytics Engineers from Works

The field of analytics engineering is gaining momentum in the engineering industry, which is dedicated to ensuring proper data collection, storage, and access. In addition, it optimizes the decision-making process through the analysis of large datasets. It is a profession that is rapidly gaining popularity and requires proficiency in data management, analysis, and visualization techniques to derive insights from data. With these techniques and strategies in place, organisations can make informed decisions and stay competitive in today’s data-driven world. At Works, our analytics engineers can help businesses gain the necessary insights to thrive in this era.

The job of analytics engineers is to create technology that empowers business users to explore, analyze and present data. Furthermore, they work closely with data scientists to perform exploratory data analysis, create and train machine-learning models, and apply statistical techniques on big data sets.

The trend towards using Extract, Load, and Transform (ELT) for Data Warehousing has greatly contributed to the rising employment and responsibilities of Analytics Engineers. These professionals have emerged in response to the shift towards modern methods of building data applications, which have replaced previous technologies such as Data Vault.

What is Involved in Analytics Engineering?

In recent years, the application of analytics engineering has become increasingly widespread, which has led to a significant rise in demand for professionals with expertise in the field. This demand is not limited to just IT companies, as organisations in various industries are acknowledging the benefits of leveraging analytics engineering capabilities.

Analytics Engineers are one of the most sought-after professionals globally, with analytics engineering roles becoming amongst the most highly demanded.

The significance of Analytics Engineers is on the rise as more and more organisations are aiming to make the most of big data solutions, including Apache Hadoop and Amazon Redshift. Their main responsibility is to create reusable assets from data generated by other team members, such as a Data Warehouse. As the demand for these skilled professionals is currently very high, and the supply of individuals who can perform these roles successfully is limited, careers in analytics engineering offer competitive salaries and benefits, even for entry-level positions.

What Responsibilities and Functions are Expected of an Analytics Engineer?

Analytics Engineers have a responsibility for ensuring the smooth operation and optimal performance of websites by overseeing the secure, organised, and efficient management of the data that powers them. Furthermore, they are expected to create a solution architecture that can effectively handle requests from businesses looking to have their profiles scraped and integrated into their own databases. They also receive requests from businesses that want to make their data available on the network for easy accessibility to users.

One of the primary tasks of a data engineer is to enhance data transformation processes to make them more efficient and speedy. This can be incredibly beneficial for organizations, as big data solutions can significantly cut down on both time and expenses.

• Collaborate with other team members to comprehend the requirements of the business.
• Develop data models and effectively communicate analytical results.
• Enhance credibility in all partnerships through Trusted Data Development.
• Oversee crucial segments of the Enterprise Dimensional Model.
• Create, extend, and design DBT code to augment the Enterprise Dimensional Model.
• Develop and maintain up-to-date architectural and system documentation.
• Oversee the Data Catalogue, a scalable resource that enables self-service analytics.
• Record the expected strategies and outcomes
• Incorporate the DataOps mindset into all activities.

What are the steps to becoming an analytics engineer?

Being the liaison between business and technology, the role of an Analytics Engineer demands a balance of technical and business expertise.

To pursue a rewarding profession as an Analytics Engineer, it is worth mentioning that you do not necessarily require specific educational qualifications. Whether you have a university degree or not, as long as you possess the necessary practical experience and skills, you can still pursue a career as an Analytics Engineer.

Many corporations seek to hire Analytics Engineers who hold a Bachelor’s or Master’s degree in Computer Science or a related field. This is typically due to a variety of reasons, such as the need for a strong comprehension of quantitative analysis and software engineering. Employers may also place significance on the ability to analyse and utilize data to create effective solutions and informed decisions.

(1) A foundation in computer programming and web development can assist in understanding Analytics engineering.

(2) Some companies may exclusively consider applicants who hold this particular degree.

Now, let’s explore the skills and methodologies you will need to excel as an Analytics Engineer:

Requirements to become an Analytics Engineer

To become a top-earning Analytics Engineer, the initial step is to develop the essential skillset. Let’s delve into what you need to learn:

1. SQL

   Structured Query Language (SQL) is a powerful programming language utilized for managing databases. It serves to control the configuration, customization and operation of databases, enabling organisations to effortlessly interact with stored data and make modifications. Any database with a SQL server integrated, like Oracle, Microsoft SQL Server, Microsoft Access, Sybase, or Google’s BigQuery data analytics platform, can be administered using this language. By means of SQL commands, users can carry out tasks such as record updates, data queries and result set retrievals.

2. Python

   Python is an interpreted programming language that features a user-friendly syntax, making it easy to comprehend and maintain. Its budget-friendly development capabilities make it a great choice for quickly building applications. Besides, Python’s standard library provides an array of functionalities, which allows users to tackle tasks with simplicity and creativity.

3. DBT

   Data Building Tool (DBT) is a command-line utility that simplifies the data adjustment process for data analysts and engineers in their respective data warehouses. This program is remarkably user-friendly, as it adheres to the Extract, Transform, Load (ETL) process. Moreover, DBT enables businesses to create transformations as queries and manage them with efficiency. This is especially beneficial for Small and Medium Enterprises (SMEs) as it tackles the complex and time-consuming ETL problems they may face.

4. Data Visualisation

   Data visualisation is a potent tool that can simplify data comprehension and foster a connection between it and the human mind. By employing visual representations like graphs or maps, data can be showcased in a manner that permits quick identification of trends, patterns, and exceptions that exist in extensive data sets. This approach can be incredibly advantageous to businesses, as it allows insights into areas that could use refinement, how certain variables impact customer satisfaction, and how to employ specific items to improve sales and future growth. When data is portrayed visually, stakeholders, business owners, and decision-makers can gain a better understanding of the data’s core and can make better-informed decisions as a result.

5. Version Control (Git)

   A version control system is a type of software that enables tracking of changes to a codebase or multiple codebases. Organisations use such systems to guarantee that if an issue arises in their production environment, they can go back to a previous version of the code. Popular examples of version control systems include Git, SVN, and CVS. Many developers consider having a comprehensive understanding of version control to be one of the most critical skills to possess, regardless of their skill level or expertise.

How to Obtain Remote Employment as an Analytics Engineer?

Developers, much like athletes, must ensure that they practise their skills effectively and consistently to achieve optimal success in their profession. Dedication and hard work to enhance their abilities over time are crucial. To facilitate this, two vital aspects are to be kept in mind: seeking guidance from those with more experience and success in their practices and monitoring the amount of practice to avoid burnout. Having a knowledgeable mentor or colleague to assist in maintaining the appropriate balance between practice and rest can be priceless.

Works is dedicated to providing optimal remote Analytics Engineer positions that satisfy your professional aspirations. You can gain valuable experience by addressing intricate technical and business-related concerns with state-of-the-art technology through our job postings. Moreover, by joining our global community of developers, you can find fully-remote Analytics Engineer roles that offer competitive salaries and outstanding opportunities for career growth.

Description of the Job

Responsibilities of the Job

• Compile complex, large-scale data sets to fulfil business specifications.
• Partner closely with data engineers and data analysts to comprehend and effectively implement requirements in the database structure.
• Compose and optimise SQL statements for reporting and analytics purposes.
• Enhancing the Analytics code base by incorporating best practices like version control and continuous integration.
• Architect the necessary infrastructure to efficiently extract, transform, and load data from the data warehouse.
• To ensure maximum scalability, internal process adjustments, such as the automation of manual operations, data delivery optimisation, and infrastructure redesigning, need to be identified, developed, and implemented. These alterations guarantee that our processes are highly efficient and our business is able to keep pace with market changes.
• Data sets that are clean and rigorously tested are supplied, and data modelling is executed.

Requirements

• Bachelor’s/Master’s Degree in engineering, computer science, or information technology (or comparable experience)
• A minimum of three years of experience in data processing/mining/analytics is necessary.
• Familiarity with ETL data pipelines, structures, and data sets, including their development and optimisation
• Proficiency in manipulating, analysing, and extracting data from extensive and varied datasets
• Proficiency in SQL and Python programming is mandatory.
• Familiarity with Google Big Query is advantageous.
• Strong interpersonal and critical thinking skills
• Proficiency in English is essential for collaborating with engineering management.
• Work a full-time schedule (40 hours per week) with a 4-hour time zone difference from US time zones.

Desirable Skills

• Proficiency in programming with R or Python is necessary.
• Thorough comprehension of data engineering technologies like Stitch, Dataform, and BI tools such as Looker, Mode, and others.
• Familiarity with the most efficient software engineering methodologies