Data Engineer Resume Example & Writing Guide

Use this Data Engineer resume example and guide to improve your career and write a powerful resume that will separate you from the competition.

Data engineers are highly sought after because they can do it all: analyze data, design databases, build applications, and more. They’re a jack-of-all-trades when it comes to working with data, and they have a unique blend of technical and analytical skills that make them valuable members of any team.

Data engineers are usually the first point of contact when it comes to analyzing data sets and building reports. They work closely with other members of a company’s engineering team to help define the ideal structure for storing data and building an efficient database that can scale with the needs of an organization.

Data engineers are also often responsible for designing and building highly customized reporting tools that empower users across an organization to access the data they need to do their jobs. Because reporting tools are so critical to any organization, data engineers need to be highly organized, detail oriented, and able to work efficiently under pressure.

Here are some tips and an example to help you write a fantastic data engineer resume that will get you noticed by recruiters.

Mary Thompson
Houston, TX | (123) 456-7891 | [email protected]

Seasoned data engineer with a passion for big data and analytics. Proven experience building data pipelines, working with Hadoop ecosystems, and developing machine learning models. Seeking an opportunity to use technical expertise and problem-solving skills to make an impact on an organization’s bottom line.

Southern Methodist University Jun '10
M.S. in Computer Science
Southern Methodist University Jun '06
B.S. in Computer Science
Company A, Data Engineer Jan '17 – Current
  • Developed a data pipeline to ingest and store 100+ TB of raw satellite imagery, processed the images using Python libraries such as Pandas, Scikit-Learn, TensorFlow, and OpenCV for feature extraction and classification.
  • Designed an algorithm that detects changes in vegetation health based on spectral information from satellites (Landsat). The algorithm was deployed at scale across multiple clients including farmers, insurance companies, and government agencies.
  • Built a web application with React/Redux to visualize crop growth over time by integrating with the data pipeline developed above.
  • Implemented machine learning models using TensorFlow to predict crop yield based on image features extracted from Landsat imagery.
  • Collaborated closely with remote teams in India and Australia to design algorithms and implement solutions remotely via Slack/GitHub/Google Docs etc..
Company B, Data Engineer Jan '12 – Dec '16
  • Implemented a data warehouse to store and process large amounts of raw data for future analysis
  • Created an automated system that monitored website performance, identifying opportunities to improve site speed by 40%
  • Developed tools to automate the collection of web page content using regular expressions in Python
  • Built a machine learning model from scratch based on historical data (using R)
  • Analyzed user behavior patterns with clustering algorithms and identified new markets worth exploring
Company C, Data Analyst Jan '09 – Dec '11
  • Conducted data analysis to support marketing initiatives and business decisions using statistical techniques and software such as Excel, SPSS, and Tableau.
  • Created data visualizations to communicate findings to non-technical staff and clients.
  • Performed quality control checks on data sets to ensure accuracy and completeness.
  • Certified Hadoop Developer
  • Certified Data Scientist
  • Cloudera Certified Administrator for Apache Hadoop

Industry Knowledge: SQL, Mapreduce, Apache Spark, Apache Hadoop, Apache Hive, Apache Pig, Apache Kafka
Technical Skills: Cloudera, Hortonworks, MapR, Amazon Web Services, Google BigQuery, Amazon Elastic MapReduce
Soft Skills: Written and Verbal Communication, Leadership, Problem Solving, Critical Thinking, Teamwork, Public Speaking

How to Write a Data Engineer Resume

Here’s how to write a data engineer resume of your own.

Write Compelling Bullet Points

When you’re writing bullet points, it can be tempting to focus on the responsibilities of your job. But if you want to stand out from other candidates, you need to go beyond that and focus on the results of your work.

For example, rather than saying you “analyzed data to identify trends and make recommendations,” you could say that you “analyzed data from customer surveys to identify trends in customer satisfaction and recommend new features based on findings.”

Notice how the second bullet point is more specific and provides more detail about what exactly you did and the results of your work.

Related: What Is a Data Engineer? How to Become One

Identify and Include Relevant Keywords

When you apply for a data engineer role, your resume is likely to be scanned by an applicant tracking system (ATS) for certain keywords. These programs look for specific terms related to the job, like “data analysis” or “predictive analytics,” in order to determine whether your skills are a match for the job. If your resume doesn’t include enough of the right terms, the ATS might discard your application.

To increase your chances of getting an interview, use this list of keywords as a starting point to help you identify the skills and experience that are most relevant to the data engineer role:

  • Data Engineering
  • Apache Spark
  • Data Warehousing
  • Data Engineering Tools
  • Data Analytics
  • Extract, Transform, Load (ETL)
  • Apache Kafka
  • Machine Learning
  • SQL
  • SQL Server
  • Apache Spark Streaming
  • NoSQL
  • Hive
  • Big Data
  • Amazon Web Services (AWS)
  • Databases
  • Hadoop
  • Apache Spark 2.x
  • Scala
  • Spark SQL
  • Python (Programming Language)
  • MapReduce
  • Tableau
  • Data Modeling
  • Data Mining
  • R (Programming Language)
  • Unix
  • Data Warehouse Architecture
  • MySQL
  • Git

Showcase Your Technical Skills

As a data engineer, you are responsible for collecting, organizing, and cleansing data in order to make it usable for analytics and reporting. In order to do this job effectively, you need to be proficient in a variety of programs and systems.

Recruiters are looking for data engineers who are skilled in programs like Hadoop, MapReduce, Hive, and Pig. They also want to see that you have experience with data mining, machine learning, and data modeling. Additionally, data engineers need to be familiar with big data platforms like Amazon Web Services (AWS) and Google Cloud Platform (GCP).

So if you have experience with any of these programs or platforms, be sure to list them on your resume. And if you’re not familiar with them, now is the time to learn them!

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