Data Engineering Manager Resume Example & Writing Guide

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

Data engineering managers are responsible for managing large data engineering teams. They set the direction for their department by defining goals and objectives, planning projects, and assigning tasks to their team members.

Data engineering managers are often responsible for overseeing the entire data engineering lifecycle, from data collection and storage to analysis and reporting. They work with other teams throughout their organization to identify opportunities for data-driven decision making and then create solutions to address those opportunities.

Data engineering managers are also tasked with ensuring that their teams are equipped with the tools they need to succeed. They often work with other managers to identify talent within the company and recruit new team members. And they monitor employees’ performance to ensure that they’re working effectively and staying engaged.

Here are some tips and an example to help you write a compelling data engineering manager resume that will stand out from the crowd.

James Smith
Chicago, IL | (123) 456-7891 | [email protected]

Seasoned data engineering manager with a record of building and leading high-performing data engineering teams. Proven ability to develop data architectures, manage big data pipelines, and optimize data processes. Experienced in working with a variety of data stores (Hadoop, Cassandra, MongoDB) and analytics tools (Hive, Pig, Spark).

Northwestern University Jun '10
M.S. in Computer Science
Northwestern University Jun '06
B.S. in Computer Science
Company A, Data Engineering Manager Jan '17 – Current
  • Led a team of data engineers to build and maintain the core infrastructure for our AI-powered marketing platform, including ETLs, APIs, ML models, and dashboards.
  • Built an automated testing framework that increased test coverage by 50% while reducing manual QA time from 2 weeks to 1 day per release.
  • Implemented a continuous integration system with over 80 integrations across engineering teams (e.g., GitHub pull requests). This reduced deployment times from hours to minutes and eliminated human error in deployments.
  • Developed a new pricing model for our SaaS product using machine learning algorithms which resulted in $1M+ revenue increase within 6 months of launch due to better customer segmentation and upselling opportunities.
  • Created a dashboard that visualized key metrics such as MRR growth rate, retention rates, churn rates, etc., allowing management to make informed decisions on future strategy & tactics.
Company B, Data Engineering Manager Jan '12 – Dec '16
  • Spearheaded the development of a data warehouse to support business intelligence and analytics initiatives, resulting in an 85% increase in productivity
  • Developed machine learning models using Python/scikit-learn for fraud detection and risk assessment (classification) purposes
  • Managed a team of 10+ developers responsible for building ETL processes, data integration, and reporting tools
  • Implemented automated testing framework on top of JUnit platform to ensure code quality before deployment
  • Collaborated with product managers, designers, and other stakeholders throughout the software lifecycle
Company C, Data Engineer Jan '09 – Dec '11
  • Developed and maintained ETL processes to load data into the data warehouse from a variety of sources.
  • Designed and implemented data models to support business analytics.
  • Created and maintained data integration jobs to keep data synchronized between systems.
  • Certified Data Engineer
  • Cloudera Certified Administrator for Apache Hadoop
  • Google Cloud Platform Big Data and Machine Learning Engineer

Industry Knowledge: Data Warehousing, Data Mining, Data Analytics, Machine Learning, SQL, Tableau, Hadoop, Spark
Technical Skills: Microsoft Office, JIRA, Confluence, Kafka, Hive, Pig, MongoDB, Redis, HBase, Vertica
Soft Skills: Teamwork, Communication, Leadership, Problem-Solving, Decision Making, Empathy, Critical Thinking, Customer Service

How to Write a Data Engineering Manager Resume

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

Write Compelling Bullet Points

When you’re writing bullet points, it can be tempting to focus on the tasks and 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 “managed data engineers,” you could say that you “increased productivity of data engineering team by 15% while reducing turnover rate by 50% over the course of one year.”

The second bullet point paints a much clearer picture of what you did and the results of your work. And it also provides a specific number to quantify your impact.

Identify and Include Relevant Keywords

When you apply for a data engineering manager role, your resume is likely to go through an applicant tracking system (ATS). This program will scan your resume for specific keywords related to the job, like “data modeling” or “ETL.” If your resume doesn’t include enough relevant keywords, the ATS might reject your application.

To increase your chances of getting an interview, use this list of common data engineering manager keywords as a starting point:

  • Data Engineering
  • Extract, Transform, Load (ETL)
  • Data Warehousing
  • Data Engineering Tools
  • Apache Spark
  • Apache Hive
  • Databases
  • Machine Learning
  • Amazon Web Services (AWS)
  • Software Development Life Cycle (SDLC)
  • Big Data
  • Data Science
  • SQL
  • Spark SQL
  • Data Analytics
  • Kafka
  • Apache Kafka
  • Hadoop
  • Apache Spark Streaming
  • Distributed Databases
  • Microservices
  • Agile Methodologies
  • HBase
  • SQL Server
  • Microservices Architecture
  • Data Management
  • Spring Boot
  • Machine Learning Toolbox
  • Programming
  • Extract, Transform, Load (ETL) Tools

Showcase Your Technical Skills

As a data engineering manager, you will be responsible for overseeing the development and implementation of data-related projects. This will require you to have a strong understanding of various data-related technologies, including databases, data mining, and data modeling. You should also be proficient in the use of specific software programs, such as SQL, SAS, and Tableau.


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