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3 Data Engineer Job Description Examples, Tips for 2022

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Stephen Greet, Co-founder

March 9, 2022

A data engineer is a technology expert who primarily prepares data for analytical or business operations uses. These engineers typically build data pipelines to combine information from multiple and varied source systems. They integrate, consolidate, and cleanse data and structure it for use in analytical applications. The goal? Make all the data easily accessible and optimize the company’s big data environment.

The data set sources, types, and quantities that engineers work with vary greatly among organizations. More data-intensive industries include healthcare, retail, and financial services. 

Data engineers collaborate with data scientists to improve data transparency and to enable better decision-making processes. They also work with business and data analysts to create systems and tools to maximize the efficient use of all data sources.

Outstanding data engineers are hard to come by. Creating a great job description is your first step, and, yeah, we get that writing one is about as exciting for you as it is for candidates to write their data engineer cover letters. See our examples and use our recommendations to write an effective data engineer job description that attracts quality candidates.

Data Engineer Job Description Example

Job details: We are looking for a talented data engineer to join our team at UltraCool Tech! Here, you'll bring advanced knowledge and experience to solve complex business issues. We'll look to your data engineering subject-matter expertise, and you will frequently contribute to the development of new ideas and methods. You’ll also get to work on complicated, interesting problems where analysis of situations and data requires an in-depth evaluation of multiple factors. We'd love for you to add your expertise to our functional project teams and participate in mission-critical cross-functional initiatives.

Responsibilities

  • Design enhancements, updates, and programming changes for portions and subsystems of data pipelines, repositories, and models for structured/unstructured data.
  • Work as a big data engineer, an individual contributor, and a team player.
  • Mine data using modern tools and programming languages.
  • Analyze, design, and determine coding, programming, and integration activities required based on specific objectives and established project guidelines.
  • Execute and write portions of testing plans, protocols, and documentation for the assigned portion of the application.
  • Identify and debug issues with code, and suggest changes and/or improvements.
  • Participate as a project team member with other data science professionals to develop reliable, cost-effective, and high-quality solutions for data systems, models, or components.
  • Collaborate and communicate with the project team regarding project progress and issue resolutions.
  • Partner with high-level individual contributors and managers.
  • Enable projects requiring data engineering solutions expertise.

Qualifications

  • Bachelor's or master's degree in computer science, information systems, engineering, or equivalent
  • 2–4 years’ data engineering experience
  • Hands-on experience on big data frameworks like Spark and Hive using Scala or Python
  • Knowledge of AWS services—Redshift, Athena, EMR, DocumentDB, S3
  • Basic knowledge of AI and data science
  • Exposure to CI/CD pipeline and GitHub
  • Experience in ETL, Data Lake, and data warehouse pipelines
  • Fluent in structured and unstructured data, its management, and modern data transformation methodologies
  • Ability to define and create complex models to pull insights, predictions, and innovations from data
  • Effectively and creatively tell stories and create visualizations to describe and communicate data insights

Benefits

  • Annual salary of $87–131k
  • Annual performance bonuses
  • Dental and vision options and employee and spouse/child life insurance
  • Short- and long-term disability protection
  • Maternity and parental leave
  • Paid holidays, vacation days, and occasional absence time
  • 401(k), pension, and stock purchase plans
  • Dependent care reimbursement account
  • Back-up child/elder care
  • Adoption assistance
  • Educational assistance
  • Robust wellness program with financial incentives

About the company: UltraCool Tech is a cutting-edge producer of consumer electronics. We focus on the latest tech and on providing users with the best interfaces and functionality. Our company is headquartered in bustling Chicago, Illinois, and we are looking for several quality data engineers to join our team. Looking for a challenging role with the opportunity for growth? Then, come work alongside our other talented data engineers and scientists at an outstanding organization. 

Big Data Engineer Job Description Example

Job details: RealAutomation Here is an innovative tech company of 20,000+ across 50+ locations worldwide. We’re committed to building a community where great people want to stay by living our values of passion, execution, teamwork, active learning, and giving back. Our data engineering team needs a highly motivated, experienced big data engineer to join our IT data engineering and analytics group located in Bangalore, India.

This role requires experiential development work on all aspects of big data engineering, data provisioning, modeling, performance tuning, and optimization. You’ll work closely with both enterprise and solution architecture teams to translate the business/functional requirements into technical specifications that drive Hadoop/HANA/BI solutions to meet functional requirements.

Responsibilities

  • Handle ingestion, transformation, and processing of data in the analytics platform.
  • Support data science, data enrichment, research, and data analysis as well as making data operationally able to be consumed by products and services.
  • Use tools provided by the analytics platform and write production-level code, adhering to and promoting best practices in the Data Engineering discipline of Infrastructure Engineering & Cloud Operations (IECO).
  • Build data-intensive solutions that are highly available, scalable, reliable, secure, and cost-effective.
  • Enable the business vision for how data strategy helps the organization be more effective in generating revenue and achieving financial goals.
  • Build and automate data pipelines using Azure, MLFlow, and Databricks.
  • Own work items from design to implementation. 
  • Implement efficient and scalable pipelines, and integrate data from multiple sources to common data models.
  • Institute design patterns that support data ingestion, data movement, transformation, aggregation, and more.
  • Collaborate with product managers, software engineers, and data scientists working toward achieving key results.

Qualifications

  • Bachelor’s degree or equivalent
  • Master of Science degree is highly desirable
  • 5+ years of data engineering experience in big data solutions
  • Experience working in a fast-paced and Agile work environment
  • Skilled at communicating and engaging with a range of stakeholders
  • Hands-on development work building scalable data engineering pipelines and other data engineering/modeling work using one or more of the following: Python, Kafka, Hadoop/Hive, and Presto
  • Expertise at querying data using SQL or other techniques
  • Excellent SQL and Analytical SQL functions knowledge 
  • Understanding of SAP HANA and knowledge of data integration platforms—Informatica PowerCenter, SAP BODS, SDI, SLT (desired but not mandatory)—will help you understand the existing landscape

Benefits

  • Salary: $93–157k/yr
  • Employee stock purchase plan
  • Medical coverage, retirement, and parental leave plans for all family types
  • Generous time off programs 
  • 40 hours of paid time to volunteer in your community
  • Rethink's Neurodiversity program to support parents raising children with learning or developmental disabilities or behavioral challenges
  • Financial contributions to your training and professional development (conferences and symposiums, workshops, classes, etc.)
  • Healthy and local-inspired snacks in our on-site pantries.

About the company: RealAutomation Here is a large enterprise that provides business technology solutions for many major companies. Our data engineering team works to solve complex business problems and create digital transformations. If you are ready to accelerate, innovate, and lead, join us as we overcome challenges and constraints to solve the problems of tomorrow today.

Senior Data Engineer Job Description Example

Job details: Do you love building and pioneering in the technology space? Passionate about solving complicated, multi-faceted business dilemmas? Motivated by a fast-paced, collaborative, inclusive, and iterative delivery environment? At Major Products Inc., you'll be part of a large group of makers, breakers, doers, and disruptors who solve real problems and meet real customer needs. We are seeking people passionate about marrying data with emerging technologies.

Responsibilities

  • Partner with and across Agile teams to design, develop, test, implement, and support technical solutions in full-stack development tools and technologies.
  • Collaborate with a team of experienced developers in machine learning, distributed microservices, and full-stack systems.
  • Leverage programming languages like Java, Scala, Python, and Open Source RDBMS and NoSQL databases and Cloud-based data warehousing services such as Redshift and Snowflake.
  • Stay abreast of the latest tech trends, experimenting with and learning new technologies, participating in internal and external tech communities, and mentoring engineering community members.
  • Work with digital product managers, and deliver robust cloud-based solutions that drive powerful experiences to help people achieve financial empowerment.
  • Perform unit tests and conduct reviews with the team to ensure code is rigorously designed, elegantly coded, and effectively tuned for performance.

Qualifications

  • Bachelor’s in computer science, data engineering, or related field
  • At least 4 years of experience in application development (internship experience does not apply)
  • At least 1 year of experience in big data technologies

Preferred Qualifications: 

  • 5+ years of experience in application development including Python, SQL, Scala, or Java
  • 2+ years of experience with a public cloud (AWS, Microsoft Azure, Google Cloud)
  • 3+ years experience with distributed data/computing tools (MapReduce, Hadoop, Hive, EMR, Kafka, Spark, Gurobi, or MySQL)
  • 2+ years of experience working on real-time data and streaming applications 
  • 2+ years of experience with NoSQL implementation (Mongo, Cassandra) 
  • 2+ years of data warehousing experience (Redshift or Snowflake) 

Benefits

  • Annual salary: $115,900–$178,325 plus 5–10% performance bonus
  • Comprehensive insurance coverage
  • Paid vacation, including 20 days per year (from day one, increasing with tenure)
  • Daycare and fitness plans available
  • 401(k), including company matching

About the company: Headquartered in Harrisonburg, Virginia, near the beautiful Shenandoah Valley, our world-class data science professionals work in an inclusive and supportive culture. Come serve at the forefront of driving a major transformation within the company—and our community.

How to Write a Winning Data Engineer Job Description

Writing a well-crafted and highly effective job description for a data engineer leaves many scratching their heads. This is often the case when it comes to many advanced and highly technical positions. Folks in technical roles possess extensive education, skills, certifications, and experience. 

When creating a job description for these types of positions, the largest challenge is typically to avoid making it too long. Keep your description focused on the most important things your data engineer must have to be successful in the role. We’ll show you how.

Refine your job description down to the crucial information 

Data engineering is a difficult job requiring the completion of many complex tasks simultaneously. Data engineers deal with many other technical and non-technical resources regularly and work with complicated tech systems to handle large amounts of data. Tight time constraints, pressure to process data quickly and efficiently, the goal to provide succinct results to data scientists and others—conveying the ins and outs of that high-level view is just not easy. 

So, first, aim to keep the attention of your readers. If potential candidates don’t completely read and understand your job posting, it’s unlikely they’ll apply. Ensuring the sections of your job description are brief and informative means your focus must be on only the pertinent job requirements for a data engineering role. It’s not the time to include anything extraneous.

Secondly, sell the job and your organization. The main objective of your job posting is to find the best person for your data engineering role. This means convincing them that your job opening and your company are superior to other jobs. You are trying to attract as many qualified applicants as possible. Often the best way to explain what a job is about is to provide examples, which remove vague and generic talk and give life to what it is your company actually does:

  • Provide an example of a specific problem they’ll need to fix (developing a custom data pipeline that integrates with third-party systems to import data sets),
  • Describe a gap they’ll fill (collaborating with data scientists to improve data feeds to the business intelligence applications), or
  • Give a reason(s) why you’re specifically hiring for the role (creating a support team to maintain external and internal data APIs).

Thirdly, we cannot stress enough the need to be concise in your writing. As with any business writing, avoid extra words and don’t beat around the busy. Each word in your posting must be meaningful. Portray the important aspects of the role in as few words as possible; avoid circumlocutions (excess words that add no meaning).

Avoid discouraging diverse candidates 

Too many requirements in your data engineering job description? You may, inadvertently, dissuade diverse candidates. Minimize this problem by limiting your requirements; only include the critical and unique needs of your company. Remove common data engineering skills or abilities, such as “strong data analysis skills.” Extra and/or less specific soft skills also tend to be unnecessary. Remember more words don’t mean better.

Many job descriptions are also plagued with biased wording or phrases. Unbiased writing is a skill that must be developed. It requires conscious effort to ensure your words and phrases will encourage diverse individuals to apply. Doing so has its rewards, according to Walden University’s writing center, as writing objectively and inclusively wins the “respect and trust from readers” and “[avoids] alienating” them. 

Edit and proofread your content

The last step in creating a great data engineering job description is thoroughly editing and proofreading. It’s a tedious task, but take the time to carefully review and edit your work. It won’t do to misspell the tech skills required or, worse, have completely missed a necessary responsibility or skill. 

We also suggest inviting as many other people as possible to review your job description. Preferably, have other data engineers (after all, they know the role best!) take a peek. You can sit on their constructive criticism for a day if it’s helpful.

You absolutely do not want to submit a job description with bad wording, grammatical and spelling errors, or inaccurate benefits or experience ranges. Spending time on careful reviews and editing will reap major benefits, save you embarrassment, and will ensure you’ve published a high-quality listing. Before you know it, outstanding data engineering resumes and applications will be flooding your inbox.

Build the frame for your data engineer job description      

Authoring a great job description can be an intimidating task, especially if it’s a job role that’s unfamiliar. If that’s the case for you, using the examples we’ve provided is good for inspiration, but starting with a solid outline may be the best way to start:

Job details: Provide a brief introduction to the company and the open position. Utilize this section to snag the reader’s attention. Offer a quick description of why your company is awesome and why they should work there. Promote the data engineering role specifically to encourage job seekers to continue reading.

What you’ll be doing: This section is also typically referred to as “Roles” or  “Responsibilities” or “Requirements.” Create a bulleted list of the most important tasks. Keep it short without leaving out anything crucial or minimizing anything that makes the job attractive. Emphasize what’s unique or cool about data engineering in your company. Follow the rules of good business writing, use active verbs, avoid jargon and filler, and be clear and brief.

Examples:

  • Collaborate with and across Agile teams to design, develop, test, implement, and support technical solutions in full-stack development tools and technologies.
  • Work with a team of developers with deep experience in machine learning, distributed microservices, and full-stack systems.
  • Leverage programming languages like Java, Scala, Python, and Open Source RDBMS and NoSQL databases and Cloud-based data warehousing services such as Redshift and Snowflake.
  • Support data science, data enrichment, research, and data analysis as well as making data operationally able to be consumed.

Qualifications: The list of items you require is key—this is where candidates will determine whether their background fits the job. List any educational, experience levels, and certifications required. Be certain that you include the absolute must-haves for the role.

We get that it’s challenging to keep the list of technical skills and qualifications short for highly technical jobs. As much as possible, stick with objective items. If you have soft skills that are really, really important for the role, do include those but be specific. Don’t include general attributes that just about anyone in the data engineering profession will have (i.e. analytical, detail-oriented, communication skills). Most candidates will usually have these things mentioned in their data engineering resume and/or cover letter.  

Examples:

  • 5+ years of data engineering experience in big data solutions
  • Experience working in a fast-paced and agile work environment
  • Skilled at communicating and engaging with a range of stakeholders
  • Hands-on development work building scalable data engineering pipelines and other data engineering/modeling work using one or more of the following: Python, Kafka, Hadoop/Hive, and Presto.
  • Ability to define and create complex models to pull valuable insights, predictions, and innovations from data
  • Effectively and creatively tell stories and build visualizations to describe and communicate data insights

Benefits: Benefits are something you always need to list in a job description. The placement in the overall format is flexible, but don’t make it the first section. Make sure to include anything your company provides that’s exceptional or unusual, such as wellness programs or an unlimited vacation policy. If your benefits are superior, you may want to place this section early in the description to grab readers’ attention. 

About the company: Your final section should contain additional information about your company. A data engineering professional will want to know your company’s strategy regarding the use of big data and how they leverage data analysis. Provide a brief description of these areas and anything else important about the data engineering role. Close on a strong note; this is your last chance to leave an impression. 

A data engineer’s possible roles and responsibilities       

Data engineers focus on collecting and preparing data for use by data scientists and analysts. They often work as part of an analytics team alongside data scientists and provide data in usable formats to the data scientists who run queries and algorithms against the information for predictive analytics, machine learning, and data mining applications. Data engineers also deliver aggregated data to business users, management, and analysts, so they can analyze it, apply the results, and build strategies to improve business operations.

Breaking a data engineer’s roles down to include on a job description might be overwhelming, so use the following list for some ideas:

General data engineering:

  • Data engineers with a general focus typically work on small teams, doing end-to-end data collection, intake, and processing. 
    • They have more skill with data structures but less knowledge of systems architecture. A data scientist looking to become a data engineer would fit well into the generalist role. 
    • A generalist data engineer for a small, food delivery service might be tasked with creating a dashboard to show the number of deliveries each day and forecast the delivery volume for the following month.

Working with different data types:

  • Data engineers deal with both structured and unstructured data. 
    • Structured data is information that can be organized into a formatted repository, like a database. 
    • Unstructured data (text, images, audio, and video files) doesn't conform to conventional data models and requires different methods. 
    • Data engineers must understand different approaches to data architecture and applications to handle both data types. 
    • A variety of big data technologies, such as open-source data ingestion and processing frameworks, are also part of the data engineer's toolkit.

Data pipeline design: 

  • Data pipeline engineering typically involves more complicated data science projects across distributed systems. 
    • Midsize and large companies are more likely to need this role.
    • A pipeline-centric project might be to create a tool for data scientists and analysts to search metadata for information.

Database management: 

  • Data engineers are often tasked with implementing, building, maintaining, and populating analytics databases. 
    • This role typically exists at larger companies where data is distributed across multiple databases. 
    • Knowledge of relational database systems, such as MySQL and PostgreSQL, is quite useful to a data engineer.
    • Data engineers work with pipelines, tune databases for efficient analysis, and create table schemas using extract, transform, load (ETL) methods. ETL is a process in which data is copied from numerous sources into a single destination system.
    • An example project might be to design an analytics database. In addition to creating the database, the data engineer would write the code to collect data from an application database and move it into the analytics database.

Subject-matter expertise:

  • Data engineers are skilled in programming languages such as C#, Java, Python, R, Ruby, Scala, and SQL. 
  • They must understand ETL tools and REST-oriented APIs for creating and managing data integration jobs.
  • Knowledge of data warehouses and data lakes and how they work is important. 
    • For instance, Hadoop data lakes that offload the processing and storage work of established enterprise data warehouses support the big data analytics efforts of data engineers.
  • Data engineers possess skills with NoSQL and Apache Spark systems for data workflows as well as Lambda architecture, which supports unified data pipelines for batch and real-time processing.
  • They have a working knowledge of Business Intelligence (BI) platforms and the ability to configure them. 
    • BI platforms can establish connections among data warehouses, data lakes, and other data sources.
  • Data engineers need familiarity with machine learning to be able to prepare data for machine learning platforms. They should know how to deploy machine learning algorithms and gain insights from them.
  • Knowledge of Unix-based operating systems (OS) is indispensable. Unix, Solaris, and Linux provide functionality and root access that other OSes don't.

Continual learning: 

  • As the data engineer profession has grown, companies such as IBM and Hadoop vendor Cloudera Inc. have begun offering certifications for data engineering. Some of the more popular data engineer certifications include:
    • Certified Data Professional: offered by the Institute for Certification of Computing Professionals (ICCP) as part of its general database professional program. 
    • Cloudera Certified Professional Data Engineer: capabilities to take in, transform, store, and analyze data in Cloudera's data tool environment. 
    • Google Cloud Professional Data Engineer: expertise in using machine learning models, ensuring data quality, and building and designing data processing systems.