Jobscan/Skills / Data engineer

Top Data Engineer Resume Skills for 2026

The data engineer skills that move your 2026 resume past the ATS, including SQL, Python, Spark, Airflow, and building reliable pipelines on cloud warehouses.

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Data engineers are the architects and builders of data infrastructure. They design, construct, test, and maintain data management systems. To excel in this role, a strong foundation in areas like SQL, data engineering principles, and computer science is crucial, alongside adaptable problem-solving and the ability to extract, transform, and load (ETL) data efficiently. These data engineer skills are vital for creating robust and scalable data pipelines that drive informed decision-making within organizations.

When writing a resume for a data engineer position, highlighting the relevant skills can significantly enhance your candidacy. Including the right data engineer skills not only demonstrates your technical proficiency but also showcases your understanding of the data lifecycle and your ability to contribute to data-driven initiatives. Tailoring your resume to underscore these skills for your data engineer resume is key to securing your next opportunity.

How we got the data

The data in this report was pulled from Jobscan’s database of more than 10 million job descriptions and 17 million resumes.

We analyzed the job descriptions to find the skills that employers want the most. Then we analyzed the resumes to see which skills appeared most frequently.

Armed with this knowledge, job seekers can easily tailor their resumes and cover letters to highlight the most relevant resume skills for each job they apply to.

top 10 data engineer Hard Skills

Most common hard skills sorted by percentage of job descriptions that list each skill. Highlight these skills with measurable results.

Management – 39%
Sales – 30%
Business – 61%
Microsoft Office – 29%
Operations – 22%
Administration – 20%
Business administration – 17%
Industry trends – 15%
Statistics – 14%
Reporting – 11%

top 10 data engineer Soft Skills

Most common soft skills sorted by percentage of job descriptions that list each skill. Highlight these skills in your summary and work experience sections.

Flexibility – 13%
Creativity – 13%
Communication – 11%
Minimal supervision – 9%
Hands-on – 9%
Accountable – 8%
Committed – 7%
Passionate – 5%
Motivated – 5%
Decision making – 4%

top 10 data engineer Skills on Resumes with High Match Rate

Skills the most qualified candidates possess. Resumes with a high Match Rate are more likely to get an interview.

SQL
ETL
Analytics
Data engineering
Business
Azure
Computer science
Management
Agile
Technical

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Top 10 data engineer skills

SQL:

SQL (Structured Query Language) is fundamental for data engineers as it’s used to manage and manipulate data in relational databases. They utilize SQL to extract, transform, and load data, as well as to query and analyze data for various purposes. Proficiency in writing efficient and complex SQL queries is essential for data retrieval and manipulation.

Sample resume bullet points:

Wrote and optimized complex SQL queries to extract and transform large datasets, improving query performance by 35%.
Designed and maintained database schemas using SQL DDL, ensuring data integrity and consistency across systems.
Utilized SQL stored procedures and functions to automate data processing tasks, reducing manual effort by 20%.

Data engineering:

Data engineering encompasses the design, building, and maintenance of data infrastructure and pipelines. Data engineers are responsible for ensuring data is accessible, reliable, and efficiently processed for analysis and consumption by other teams. This involves selecting the right technologies, designing data models, and implementing ETL or ELT processes.

Sample resume bullet points:

Designed and implemented a scalable data lake solution on Azure Data Lake Storage, enabling efficient storage and processing of diverse data types.
Developed robust ETL pipelines using Apache Beam and Google Cloud Dataflow, ensuring timely and accurate data delivery for business intelligence.
Led the migration of legacy data systems to a modern data warehouse, improving data accessibility and reducing maintenance costs by 15%.

Computer science:

A strong foundation in computer science provides data engineers with the theoretical knowledge and problem-solving skills necessary to tackle complex data challenges. This includes understanding algorithms, data structures, and system architecture, which are crucial for designing efficient and scalable data solutions.

Sample resume bullet points:

Applied computer science principles to design an optimized data indexing strategy, reducing data retrieval times by 45%.
Developed a distributed data processing framework using Python and Dask, improving the efficiency of large-scale data analysis.
Utilized knowledge of data structures and algorithms to implement efficient data transformation processes, enhancing pipeline performance.

Flexibility:

Flexibility is a crucial soft skill for data engineers as the data landscape is constantly evolving. They need to be adaptable to new technologies, changing requirements, and different data sources. Being flexible allows them to learn quickly and adjust their approaches to meet the dynamic needs of data projects.

Sample resume bullet points:

Demonstrated flexibility by quickly learning and implementing new cloud-based data services, contributing to a 20% increase in team productivity.
Adapted ETL pipelines to accommodate changes in data sources and formats, ensuring continuous data flow and accuracy.
Showed versatility in working with various data processing frameworks and programming languages to meet diverse project requirements.

ETL:

ETL (Extract, Transform, Load) is a core process in data engineering, involving extracting data from various sources, transforming it into a usable format, and loading it into a target system like a data warehouse.1 Data engineers need expertise in designing, building, and optimizing ETL pipelines to ensure data quality and accessibility for analysis.

Sample resume bullet points:

Designed and implemented ETL workflows using Informatica PowerCenter, processing over 500 million records monthly with a data quality score of 99.9%.
Optimized existing ETL processes using SQL and Python scripting, reducing execution time by 30% and improving resource utilization.
Developed and maintained ETL pipelines for real-time data ingestion using Apache Kafka and Flink, enabling timely insights for business stakeholders.

Business:

Understanding the business context is important for data engineers to ensure that their work aligns with organizational goals and provides value. They need to understand how data is used by different business units and how their data infrastructure can support business intelligence and decision-making processes.

Sample resume bullet points:

Collaborated with business analysts to understand data requirements and designed data models that supported key business metrics, leading to improved reporting accuracy.
Translated business needs into technical specifications for data pipelines, ensuring that data solutions effectively addressed business challenges.
Developed data solutions that directly supported a new business initiative, resulting in a 10% increase in operational efficiency.

Java:

Java is a versatile and widely used programming language in the data engineering field, particularly for building scalable and robust data processing applications. Experience with Java and related frameworks like Apache Hadoop or Apache Spark can be highly valuable for data engineers working with big data technologies.

Sample resume bullet points:

Developed scalable data processing applications using Java and Apache Spark, improving the efficiency of large-scale data analysis by 40%.
Implemented data ingestion frameworks in Java that integrated data from multiple disparate sources into a centralized data lake.
Utilized Java and Spring Boot to build robust and maintainable data APIs for internal data consumption.

Data science:

While distinct from data science, a foundational understanding of data science principles is beneficial for data engineers. It allows them to better understand the needs of data scientists, build appropriate data pipelines for machine learning models, and contribute to the overall data-driven initiatives of an organization.

Sample resume bullet points:

Collaborated with data scientists to design and build data pipelines optimized for machine learning workflows, reducing feature engineering time by 20%.
Implemented data quality checks and validation processes to ensure the integrity of data used for data science models, improving model reliability.
Developed data extraction and transformation processes to support specific data science projects, contributing to the successful deployment of predictive models.

Creativity:

Creativity in data engineering involves thinking outside the box to solve complex data integration and processing challenges. It’s about finding innovative solutions to improve efficiency, data quality, and the overall performance of data systems.

Sample resume bullet points:

Leveraged creativity to develop a novel data deduplication process that improved data accuracy by 15% and reduced storage costs.
Devised an innovative approach to data monitoring and alerting, proactively identifying and resolving data pipeline issues, reducing downtime by 25%.
Applied creative problem-solving skills to optimize a legacy data integration process, resulting in a 30% improvement in processing speed.

Machine learning:

Familiarity with machine learning concepts and the data requirements of ML models is increasingly valuable for data engineers. They are often responsible for building the data pipelines that feed machine learning algorithms, ensuring data quality and efficient delivery for model training and deployment.

Sample resume bullet points:

Designed and implemented data pipelines to support the training and deployment of machine learning models, ensuring real-time data availability.
Optimized data storage and retrieval for machine learning features, improving model training times by 20%.
Collaborated with machine learning engineers to troubleshoot data-related issues in model development and deployment, ensuring smooth operation of ML systems.

Tips for using data engineer skills on a resume

Create a bulleted resume skills section

Use a simple, clean format that highlights your skills and experiences. Focus on readability by using bullet points and short phrases to present your skills. Properly organize your resume into education, experience, and skills sections.

“Adding a skills section to your resume is a great way to draw the recruiter’s attention to your most relevant strengths and competencies.”

– Ashley Watkins, NCRW, NCOPE, Job Search Coach

For example, data engineer resume bullet points might look like this:

Developed and maintained ETL pipelines using Apache Spark and Python, processing over 10 TB of data daily with 99.9% uptime.
Designed and implemented a scalable data warehouse solution on AWS Redshift, improving query performance by 40% for business intelligence dashboards.
Utilized strong SQL skills to optimize complex database queries, reducing execution time by 60% and enhancing data retrieval efficiency.
Collaborated with data scientists to build robust data pipelines for machine learning models, resulting in a 15% improvement in model accuracy.
Leveraged creativity to troubleshoot data integration issues, developing innovative solutions that reduced data inconsistencies by 25%.

Hitting all the top job requirements with your skills list will help you rank highly for a keyword search within an applicant tracking system. But don’t stop there. Add context for every skill elsewhere in your work experience.

If a recruiter is excited by your customer service skills, for example, the first thing they’ll do is skim your work experience to figure out when, how, and how much you used that skill.

Highlight skills and achievements in your work experience section

As you list your responsibilities, it’s also important to highlight your specific achievements wherever you can.

“For soft skills, it’s often more effective to demonstrate them in the context of your past work experience. Instead of merely stating “excellent team player”, you are better off saying “collaborated with a cross-functional team of 6 on a new product launch that boosted sales by 30% in one year”. Examples of specific accomplishments or business outcomes speak louder than buzzwords.”

– Ana Lokotkova, Career Coach and Advisor

Instead of saying,

“Worked with data.”

You could say,

“Designed and implemented data ingestion processes using SQL and ETL tools, improving data availability for analytics by 30% and ensuring data accuracy.”

This demonstrates your level of expertise with the skills you listed. It gives the recruiter more reason to be interested in you as a result.

Break resume skills sections into categories

If you’re applying for a role requiring a broad skillset, categorize your skills.

“If you opt to include a designated skills section on your resume, include up to 10 of your core competencies. Excessive skills lists are overwhelming and sometimes confusing to the reader.”

– Kelli Hrivnak, Marketing and Tech Recruiter

Technical Skills: SQL, Data engineering, Computer science, ETL, Java, Data science, Machine learning
Soft Skills: Flexibility, Business, Creativity

Quantify your accomplishments

Use numbers to quantify your accomplishments wherever possible. This helps potential employers understand your abilities and the impact you can have on their organization.

“Recruiters and hiring managers are looking for relevancy of how and when you applied those skills, so provide examples of this in your experience section. Bonus tip: Don’t just insert the skill like a task–include results.”

– Kelli Hrivnak, Marketing and Tech Recruiter

Instead of saying,

“Improved data processing speed.”

You could say,

“Optimized data processing workflows using Spark and parallel processing techniques, reducing batch processing time by 50%.”

Tailor your resume to the job description

Read the description carefully and emphasize the relevant skills and experiences. Highlight the skills that the employer seeks and provide examples of how you’ve used those skills in your previous roles. Doing so can demonstrate that you’re a strong fit for the position and increase your chances of being invited to an interview.

“The ‘one-size-fits-all’ approach doesn’t work when it comes to your resume. For every job application, tailor your skill set to match the job description. Most companies use Applicant Tracking Systems (ATS) that filter candidates based on keywords and skills listed in the job description. So, research the role you’re applying for and distill the skills required.”

– Ana Lokotkova, Career Coach and Advisor

In order to do this, “you must first understand what skills are most important for the target role,” says Ashley Watkins.

Sample job description for data engineer:

We are seeking a skilled and motivated Data Engineer to join our growing data team. The ideal candidate will have a strong background in data warehousing, ETL processes, and database technologies. Responsibilities include designing, building, and maintaining scalable data pipelines, ensuring data quality, and collaborating with data scientists and analysts to support their data needs. Proficiency in SQL, Python, and cloud data platforms (e.g., AWS, Azure, GCP) is required. Experience with big data technologies like Spark or Hadoop is a plus. The candidate should also demonstrate strong problem-solving skills and the ability to adapt to evolving technologies.

Resume bullet points tailored to the job description:

Designed and implemented scalable ETL pipelines on AWS using Python and Apache Airflow, ensuring reliable and efficient data delivery for downstream analytics.
Developed and maintained data warehouse solutions using SQL and AWS Redshift, optimizing query performance and ensuring data integrity.
Collaborated with data scientists to build robust data pipelines for machine learning initiatives, supporting the development of predictive models.
Utilized strong SQL skills to extract, transform, and load data from diverse sources, ensuring data quality and accuracy for business intelligence reporting.
Demonstrated flexibility in adapting to new cloud data technologies and implementing best practices for data management and governance.

“Focus on the sought-after and in demand skills. A great way to figure out what is currently in demand is by researching current job openings from your preferred companies and reading through the posting. Pay close attention to the preferred requirements section and build your skills section based on this list.”

– Chelsea Jay, Career & Leadership Development Coach – Seasoned and Growing

Bonus Tip: Use action verbs

Start each bullet point with an action verb. An action verb expresses an action, such as “create,” “build,” “manage,” “lead,” or “implement.”

Action verbs grab the reader’s attention and paint a vivid picture of what you accomplished at work.

Action verbs make your resume more interesting to read. They also show the kind of can-do attitude that employers are looking for.

FAQs

What skills does data engineer need?

Data engineers require a blend of technical and soft skills. Technically, they need strong abilities in SQL, data modeling, ETL processes, and often programming languages like Python or Java. Familiarity with big data technologies and cloud platforms is also crucial. Soft skills such as problem-solving, communication, and the ability to work collaboratively are essential for success in this role.

What technical skills do you need to become data engineer?

Key technical skills for a data engineer include proficiency in SQL and database management systems, experience with ETL tools and frameworks (e.g., Apache NiFi, Talend), knowledge of programming languages like Python or Java, and familiarity with data warehousing solutions (e.g., Snowflake, Redshift). Increasingly, experience with big data technologies (e.g., Spark, Hadoop) and cloud platforms (AWS, Azure, GCP) is also highly valued.

What are the most important data engineer job skills to have on your resume?

The most important data engineer job skills to highlight on your resume include a strong command of SQL, demonstrable experience in designing and implementing data pipelines (ETL), a solid understanding of data warehousing principles, and proficiency in at least one relevant programming language. Highlighting your experience with big data technologies and cloud platforms, as well as your problem-solving abilities, will also significantly strengthen your application.

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