The first step to landing a data engineering job at your dream company is to have an amazing data engineer resume that tells recruiters and hiring managers why you’re a must-hire. Unlike creating a tech resume for general engineering roles, writing a data engineer resume will require you to capture specific experience, skills, and qualifications for a specialized data science role.
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How do you create the best data engineer resume to grab a recruiter’s attention and land a tech interview at a FAANG or Tier-1 tech company? In this article, we guide you through the process of creating a winning data engineer resume with tips and samples of the best data engineer resume formats.
We’ll cover:
- Data Engineer Resume — ATS- and Recruiter-Friendly Formats and Layouts
- How to List Key Sections on Your Data Engineer Resume
- Data Engineer Resume Sample/Template
- FAQs on Writing a Data Engineer Resume
Data Engineer Resume — ATS- and Recruiter-Friendly Formats
The right way to format your data engineer resume is to ensure that it is ATS-readable and recruiter-friendly.
ATS-Readable Data Engineer Resume
To prepare a resume that an ATS can read, ensure that it:
- Contains relevant keywords that match the job description and requirements
- Is structured in clear sections with relevant section titles
- Uses the right font with key information presented in bulleted lists
- Uses the right file format (word doc or pdf are the generally accepted formats)
- Uses a standard, professional resume layout
- Uses a minimalistic design; don’t use fancy templates, columns, tables, icons, visuals, or images to present important information as an ATS does not easily read this
Recruiter-Friendly Data Engineer Resume
To prepare a resume that grabs a recruiter’s attention at a glance, focus on two key aspects:
- Resume Content: This refers to information that highlights the most important and relevant aspects of your profile, including key details about your professional profile.
- Resume Structure or Layout: This refers to the way the information is presented.
We delve into both these aspects in detail through the rest of this guide.
Recommended Reading: Roles and Responsibilities of a Google Data Engineer
How to List Key Sections on Your Data Engineer Resume — Experience, Skills, Education, and More
Your resume should include pertinent, relevant information on the following key sections:
Header/Personal Information
This section should include:
- Your name and Professional title
- Contact details, i.e., your email id, location, social media handles, and, if applicable, a link to your project or tech portfolio.
Work Experience
This is the most crucial section of your resume. Recruiters generally begin reading resumes by scanning a candidate’s work history. Follow these important steps when listing experience on your data engineer resume:
- Customize your resume to showcase the most relevant experience in reverse-chronological order
- Use bulleted lists for clear job role descriptions; write concise, complete descriptions of one or two lines each.
- Use the active voice and action verbs such as “Implemented,” “Initiated,” “Designed,” etc., to grab recruiters’ attention.
- List your experience in terms of contributions made, quantifying them where possible, instead of compiling a laundry list of tasks and responsibilities.
Projects and Awards/Achievements
Professional and personal projects can be listed under the work experience section or under a separate section. Showcase your projects in a structured manner mentioning your role and technologies used.
Awards and achievements can be listed under work experience or under a separate section to highlight expertise. List only relevant awards and achievements.
Skills
This is a critical section of your data engineer resume. Ensure it is placed prominently on your resume, preferably as a separate section. Alternatively, you can highlight skills in your work experience section, through projects, awards, achievements, and education and certifications or as part of your resume objective or headline.
List a balanced number of hard skills or technical skills and soft skills and group them separately; group similar skills; you can do this by the level of proficiency, expertise, technology, or knowledge area.
- By Proficiency: Advanced, proficient, expert, working knowledge, beginner, conceptual knowledge, hands-on experience, etc.
- By Type, Technology, Tools: Programming languages, database technologies, cloud technologies, tools, frameworks, platforms, etc.
List soft skills as these are an integral part of data engineering roles where you are bound to work with different stakeholders to execute your tasks.
Top soft skills for a data engineer include:
Include 10-15 relevant tech skills and highlight in-demand skills. Some of the top skills you can list on your data engineer resume:
Certifications
List relevant and valid professional certifications under the section ‘Education’ or as a separate section if you have multiple certifications.
Some top certifications you can list on your data engineer resume are:
- DASCA Senior Big Data Engineer
- AWS Certified Big Data Specialty
- Google Cloud Professional Data Engineer
- Microsoft Certified: Azure Data Engineer Associate
- CCDAK Confluent Certified Developer for Apache Kafka
- Apache Cassandra Developer Associate Certification
Recommended Reading: Amazon Data Engineer Salary
Education
This is also an important section of your data engineer resume since most data engineering roles require a bachelor’s or master’s degree in computer science, engineering, or a related field. You don’t have to go into detail about your degrees or courses unless you’re a new graduate, but cover the following pertinent details:
- Degree Title (Bachelor’s, Master’s, Ph.D. + Area of specialization)
- Date obtained; period of study (you can include incomplete degrees that you’re currently pursuing)
- Conferring Authority (University or Institution)
Other Optional Sections
The following optional sections can be included on your data engineer resume if the information is relevant and will make an impact on the recruiter. However, ensure you utilize available space for the most important sections and details first.
- Career Objective/Resume Headline: This is useful if you have a long resume. Keep it to about two or three sentences using keywords that help you match the job description.
- Publications, Associations, etc.: Information that establishes you as a thought leader can give a boost to your resume. Similarly, being a part of related associations will portray you as an active member of the data engineering community and exhibit your passion and interest in the field.
- Interests, Volunteer Work, Extracurricular Activities: Mention only relevant information that can showcase your skills and substantiate your work experience.
Data Engineer Resume Sample/Template
Outlined below is an example of how to write a data engineer resume highlighting sections and formatting tips as discussed above.
Note: The above contains representative information of a data engineering profile. You should ensure your resume contains as much relevant information as possible by customizing your profile to suit the job description and company requirements.
In the next section of this guide, we understand how to customize resumes to suit different data engineering roles.
Junior Data Engineer Resume or Entry-Level Data Engineer Resume
To write an entry-level data engineer resume for limited or no experience when starting out on your data engineering career path:
- Showcase projects and internships in lieu of work experience.
- Under “Education,” list academic courses undertaken to show that you’re aware of relevant knowledge areas needed for the role. List your GPA only if it’s impressive.
- Under “Skills,” mention skills by level of proficiency, highlighting skills you can speak confidently on.
- If you’re switching careers to enter the data engineering space, focus on tasks or technical skills that you can leverage to match the prospective job.
Mid-Level Data Engineer Resume
To write a mid-level data engineer resume for around 5-10 years of experience:
- Show your ability to apply conceptual knowledge and make valuable contributions that directly or indirectly impact team or business outcomes.
- If the data engineering role is purely technical, play up your technical skills. If the role calls for leadership, management, and other people management responsibilities, highlight your technical and interpersonal skills.
- Focus on the “Work Experience” section; your practical skills will be a deciding factor in whether you’ll be considered for an interview. Highlight instances in which you displayed managerial competence, even if these weren’t your primary responsibilities.
- Showcase relevant educational and professional development qualifications as many companies prefer candidates with a relevant degree or certification for mid and higher-level data engineering positions.
Recommended Reading: Apple Data Engineer Salary
Lead Data Engineer Resume or Senior Data Engineer Resume
To write a lead data engineer resume for around 10+ years of experience:
- Showcase advanced technical skills and experience in providing technical direction, especially on mid-large scale projects for a technical career track, and strong interpersonal and behavioral skills for a people management track.
- Showcase knowledge of the latest technologies and data engineering trends.
- Indicate the size and scale of projects and teams you’ve worked with as scalability is a key competency companies seek in senior data engineers.
- Don’t elaborate on early career roles.
- Showcase advanced educational degrees or certifications since many companies prefer highly qualified engineers for senior-level data engineering jobs.
- A resume headline or summary is useful for highlighting key aspects of your profile for a senior-level data engineer resume that can run two pages long.
Similarly, customize your Principal Data Engineer Resume or Staff Data Engineer Resume to showcase skills and experience that indicate your ability to function in a senior-level data engineering role.
Azure Data Engineer Resume
To write an Azure data engineer resume:
- Tailor your resume to showcase Azure work experience and skills
- List Azure skills separately
- Highlight Azure certifications
- Utilize a resume headline to identify yourself as an Azure Data Engineer
- Research the role and job description and utilize Azure-specific keywords and skills
Similarly, tailor your Python Data Engineer Resume, AWS Data Engineer Resume, Big Data Data Engineer Resume, or any other data engineer resume based on a specific technology by highlighting experience, skills, and certifications in the said area.
FAQs on Writing a Data Engineer Resume
Q1. How long should a data engineer resume be?
Your data engineer resume should be one or two pages long, depending on your experience levels. Experienced or senior data engineers can have a two-page resume but ensure the most important information is presented on the first page.
Q2. What are some of the top skills to include on a data engineer resume?
Python, Scala, Go, ETL and Data Pipeline Tools, Big Data Tools, Hadoop, Hive, Database Technologies, Cloud Engineering, Azure, AWS, Kafka, Apache, PySpark, Data Warehouses, Data Platforms, ML, etc. are some of the most in-demand data engineering skills.
Q3. Should I include soft skills in a data engineer resume?
Yes, interpersonal skills are important for data engineers who work with multiple stakeholders and data scientists to ascertain business requirements and develop the right infrastructure and systems.
Q4. How is a data engineer resume different from a software engineer resume?
Software engineer resumes tend to cater to generalized software engineering roles. In contrast, a data engineer resume has to contain information about data engineering roles that require specific skills, experience, and qualifications.
Q5. What is a data engineer’s job?
To write a data engineer resume, you have to understand the roles and responsibilities of a data engineer. Data engineers are responsible for building and maintaining the data architecture, systems, and infrastructure that enables companies to source, gather, store, access, and analyze data for business insights.
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