Last updated on 9 Feb 2023
Want to stand out and attract the best Amazon marketing specialists? Here’s a job description template and guide to help you attract the right candidates with your required skill sets.
We uncover everything you need to know to create the optimal job description to attract the best fit for your company.
See below the sample template, which should only serve as a guide and may be edited according to your company’s needs. Scroll down further to see a comprehensive description of the role we provided for you.
This is where you make a first impression with prospective hires. In this section, write a concise paragraph about your company and what candidates can expect from working with your team.
Here are a few details you may include in your introduction:
Products & services
Company’s mission & goals
Company culture & work environment
As a Data Engineer, you will be responsible for enabling a data-driven approach to optimization by sourcing, maintaining, and ensuring the availability of data used to drive marketing insights to optimize marketing investments. You’ll integrate different data processing approaches to support standard business intelligence, as well as decision automation and machine learning requirements.
Provide leadership and support for the development of an integrated digital marketing data foundation that will enable extensive business intelligence and machine learning for digital marketing and extended user communities.
Focus on data comprising digital marketing and other supporting data (e. g., weblogs, sales-related data, customer and contact data, social data, third party purchase data, etc.).
Assist planning, designing, building, and documentation of digital marketing data.
Work with digital marketing and IT teams to ensure high quality, on-time deliverables that meet usability, scalability, quality, and performance standards.
Provide hands-on technical support for development, research, and quality assurance testing.
Perform due diligence checks to ensure quality.
Support change management efforts, including proactive communication with other teams and users.
Proven experience as a Data Engineer or relevant role.
Building Data Pipelines, with hands-on development of scripts using various programming languages including SQL, Python, Unix Shell, HQL, Spark Programming, Java.
Understanding and experience with structured/unstructured database environments and Big Data environments.
Hands-on experience using Microsoft products (Excel, Word, PowerPoint, Access).
Good understanding of digital marketing and systems concepts, processes, and data.
Strong verbal and written communication skills.
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Let’s first define what data engineering is. Data engineering is the process of designing and implementing optimal systems for data collection, segmentation, and analysis at scale. Often involving machine learning, it allows for successive interpretation and data science.
This field is usually applicable to a broad range of industries as companies are usually in need of collecting large sums of data. With the right tech and experts, these data sets would be readable and digestible by data scientists and analysts. Basically, data engineers translate the language of big data for us.
Thus, your job description must be written with a deep understanding of the role specifically in your company and industry. Clearly know and communicate how the role will fill some of your organization’s gaps.
Simply put, big data is a voluminous amount of data with more complex data sets from various different sources. In fact, the three Vs of big data are: variety, volume, and velocity – in which it posses these much more than “normal data”.
Since organizations (perhaps like yours) are all digging into big data, it has changed the way businesses work. Thus, there is a growing need for data engineers to manage these massive data sets.
Although some companies don’t require data engineering certifications, keep an eye out for candidates with the certifications below. These are credible certificates that are hard-earned:
Google Professional Data Engineer
Microsoft Certified: Azure Data Engineer Associate
Data Science Council of America (DASCA) Associate Big Data Engineer
AWS Certified Data Analytics -- Specialty
Cloudera Certified Professional (CCP) Data Engineer
Arcitura Certified Big Data Architect
Data engineers reformat big data into a readable structure ready for analysis by optimizing, implementing, maintaining, and testing large data sets and database systems. They typically work closely with data scientists, IT, or digital marketing teams since they provide structural solutions.
A data engineer’s work is imperative for data scientists/analysts to be able to provide insights that can yield informative business decisions.
That said, data engineers are often confused with data scientists, so let’s differentiate the two roles below.
Although both deal with data, the biggest difference between the two lies in what they do with the data sets.
As mentioned, data engineers transform big data architectures into readable databases and form efficient large-scale collection systems. Think of it this way – they are the first line of defense. The data first passes through their hands before moving on to data scientists.
Afterward, data scientists would receive the data that has been cleaned and filtered by data engineers. Thus, their role is to use sophisticated analytics and statistical techniques to form forecasts from the data received. They often develop prescriptive and predictive models out of these data sets with the use of statistical algorithms and machine learning.
That said, both roles are vital for companies to gain insights that can increase their success and guide them with the right decisions.
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According to Glassdoor, the Data Engineer’s average salary typically ranges from $88,000 to $142,000 annually.
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