Data Engineer Skills
The key hard and soft skills required for a data engineer job in 2023 based on our database of over 10 million real job listings.
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Data engineer skills include building and maintaining data pipelines, which ensure the smooth flow of data within a company’s systems. They make sure the data is accurate, reliable, and available when needed for analysis or reporting..Data engineers work across a variety of industries, including finance, healthcare, advertising, technology, and retail.
The job of data engineers is to make sure the company’s data infrastructure is robust, secure, and scalable. This involves monitoring data quality and resolving any issues. Data engineers also develop strategies to optimize data storage, processing, and analysis for maximum efficiency and performance.
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 skills for each job they apply to.
top 10 data engineer Hard Skills
top 10 data engineer Soft Skills
top 10 data engineer Skills on Resume with High Match Rate
- Python
- Engineering
- Communication
- Analytics
- Programming
- Professional
- Data Science
- Computer Science
- Machine Learing
- Architecture
Top 10 data engineer skills
Examples of how to write this skill on your resume:
- Proficient in Python programming language, with extensive experience in developing and implementing data pipelines and ETL workflows.
- Created and deployed scalable machine learning models using Python libraries like NumPy, Pandas, Scikit-learn, and TensorFlow.
- Developed custom data analysis scripts using Python to help business stakeholders make data-driven decisions.
- Conducted thorough code reviews in Python scripts, resulting in a 30% reduction in data processing time.
Examples of how to write this skill on your resume:
- Designed and developed a real-time data processing pipeline that reduced data latency by 50% and improved overall system efficiency by 40%.
- Led a team of engineers to implement continuous integration and deployment (CI/CD) workflows.
- Built and maintained automated testing frameworks for data pipelines and web applications.
- Implemented a data governance framework that ensured compliance with regulatory requirements and company policies.
Examples of how to write this skill on your resume:
- Developed data pipelines using Apache Spark to process and analyze large volumes of data in real time.
- Analyzed large datasets using SQL and Python to identify trends and insights that informed business decisions.
- Analyzed customer data to identify cross-sell and upsell opportunities, resulting in a 12% increase in sales.
- Conducted A/B testing and analyzed results to optimize website user experience, resulting in a 20% increase in user engagement.
Examples of how to write this skill on your resume:
- Developed a data pipeline using Python and Apache Airflow resulting in a 50% reduction in data processing time.
- Created a data visualization tool using Python and Dash, enabling stakeholders to analyze data and make informed decisions.
- Built a recommendation engine using Python and TensorFlow, increasing user engagement by 25%.
- Optimized SQL queries for a data warehouse resulting in a 60% increase in query performance.
Examples of how to write this skill on your resume:
- Collaborated with the data science team to build a recommendation engine using Python and Scikit-learn.
- Created a real-time dashboard using React and Node.js to display key metrics, resulting in faster decision-making by the business team.
- Implemented data partitioning and indexing on a PostgreSQL database, improving query performance by 200%.
- Built a self-service data exploration platform using Apache Superset, enabling non-technical users to analyze data on their own.
Examples of how to write this skill on your resume:
- Designed a user-friendly dashboard for analyzing real-time data streams.
- Conducted user research and designed wireframes for a new data visualization tool.
- Designed and implemented an automated ETL pipeline for a large-scale data warehouse.
- Redesigned the user interface of an existing data processing application, resulting in a 40% reduction in user error rates.
Examples of how to write this skill on your resume:
- Developed and deployed Hadoop clusters for a variety of data processing tasks, resulting in a 50% increase in data processing speed.
- Designed and developed data pipelines using Hadoop ecosystem tools like Pig, Hive, and Sqoop.
- Optimized MapReduce jobs by tuning Hadoop configuration parameters, resulting in a 40% reduction in processing time.
- Implemented Hadoop security measures, including Kerberos authentication and encryption, to secure sensitive data.
Examples of how to write this skill on your resume:
- Analyzed large datasets using SQL and NoSQL databases to identify key insights and optimize business processes.
- Built and maintained a data warehouse on Amazon Redshift, enabling the company to analyze data more efficiently and effectively.
- Created dashboards using Tableau to provide real-time visualizations of key performance indicators for senior management.
- Designed experiments to optimize the performance of recommendation algorithms, resulting in a 15% increase in click-through rate.
Examples of how to write this skill on your resume:
- Developed and implemented machine learning models to automate anomaly detection, resulting in a 30% reduction in false positives.
- Improved natural language processing models by implementing pre-processing techniques such as stemming and stop word removal.
- Conducted feature engineering on data sets to extract meaningful features for training machine learning models.
- Built a predictive maintenance system using regression models to predict equipment failure, reducing maintenance costs by 25%.
Examples of how to write this skill on your resume:
- Developed a real-time data ingestion system using Kafka that processed over 500K messages per second.
- Implemented data quality checks and monitoring systems that improved data accuracy by 10% and reduced data inconsistencies by 25%.
- Created and maintained data warehouses and data lakes using tools such as Snowflake and Amazon S3.
- Built and maintained ETL pipelines using Apache Spark that processed over 10 TB of data daily with 99.9% uptime.
Optimize your resume in minutes
5 Tips for Writing a Data Engineer 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.
For example, a data engineer’s bullet points might look like this:
- Proficient in programming languages such as Python, Java, or Scala.
- Familiarity with distributed systems, cloud computing, and big data technologies.
- Knowledge of SQL and NoSQL databases, data warehousing, and data modeling.
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 Python 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 in your work experience section, it’s also important to highlight your specific achievements wherever you can.
For example, if you reduced data processing time by 50%, be sure to mention this accomplishment.
Instead of saying,
“Optimized data pipelines.”
You could say,
“Optimized data pipelines and reduced processing time by 50%.”
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.
For example, someone applying for a role as a data engineer might benefit from segmenting their skill lists as follows:
Technical Skills
- Python
- MySQL
- AWS
- Azure
- Google Cloud
Soft Skills
- Analytical thinking and problem-solving
- Collaboration and teamwork
- Communication and presentation skills
- Attention to detail and accuracy
- Time management and organization
Quantify your achievements
Use numbers to quantify your achievements wherever possible. This helps potential employers understand your abilities and the impact you can have on their organization.
Instead of saying,
“Designed and implemented ETL pipelines for processing large datasets.”
You could say,
“Designed and implemented ETL pipelines for processing large datasets, resulting in a 50% reduction in processing time and increasing data availability by 75%.”
By using numbers, you give hiring managers a better sense of your value.
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.
Here’s an example of how to tailor an account manager resume to a job description:
Job Title: Data Engineer
Requirements:
- Experience with data modeling, ETL, and data warehousing
- Proficiency in SQL and Python programming languages.
Tailored Resume Description:
- Designed and implemented ETL pipelines using Apache Airflow, resulting in a 40% reduction in processing time.
- Developed data models for complex systems using ER diagrams and UML diagrams.
- Built and maintained data warehouses on AWS Redshift and Snowflake platforms.
- Proficient in SQL programming, with expertise in writing complex queries and stored procedures.
- Proficient in Python programming, with experience in developing data pipelines using libraries such as Pandas and NumPy.
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
The key skills needed for a career as a data engineer are Python, Apache Hadoop, analytics, engineering, programming, computer science, design, data science, and machine learning.
Data engineers should also possess strong communication and collaboration skills, as they often work closely with other data professionals.
Data engineers require a stronger foundation in database technologies while software engineers are more focused on developing software applications.
Another difference is that data engineers typically work more closely with data scientists and analysts to design and implement data pipelines. Software engineers are more involved in developing the user interface and overall architecture of software applications.
In smaller companies, data engineers may need to have a broader range of skills. This is because they may be working alone. In larger companies, data engineers may be part of a team and their skills may be more specialized.
Regardless of company size, data engineers will always need to have a strong foundation in database technologies, programming languages, and data processing frameworks.
Data engineers focus on the development and maintenance of the infrastructure needed to store, process, and manage large volumes of data. Data scientists focus on analyzing and interpreting that data to derive insights and drive business decisions.
Some of the key skills that are assessed in a data engineering interview include:
- Programming languages such as Python, Java, or Scala.
- Knowledge of big data frameworks like Hadoop, Spark, or Kafka.
- Experience with ETL (extract, transform, load) processes and data pipeline design.
- Familiarity with cloud-based technologies like AWS, GCP, or Azure.
- Knowledge of software engineering principles and practices, such as version control, testing, and debugging.
