Top Places to Work for Data Engineers

We identify organizations in the U.S. that will help you develop your career in data engineering.

By Jike Chong, Ben Lorica, and Yue Cathy Chang.

In our previous post (“Top Places to Work for Data Scientists”), we compiled lists of U.S. organizations that can help data scientists identify great teams to join at various stages of their career.  

What would constitute a good place to work for a data engineer? How is the outlook of data engineering as a field? These are important questions to ponder as data engineering practitioners witness the field going through a phase of high growth of 33% per year

Data engineers design, implement, deploy, and maintain data solutions to meet business needs. Their responsibilities start with sourcing data and include ingesting, transforming, and storing data in the data lake, data warehouse, or data lakehouse. 

The need for robust, production-level data infrastructure is becoming more apparent as data analytics and data science projects are generating enterprise value.

Successful data engineers are proficient technically and can understand business drivers, translate business drivers into data requirements, and comply with data privacy policies and regulations. Companies depend on them to protect data quality through data pipeline deployment and operation, data governance through data catalog and lineage management, while balancing implementation trade-offs for master data, reference data, and data streams. 

What are some companies that can provide a good platform for data engineers to build their careers? Let’s look at how to quantify this assessment.


As we look to evaluate U.S. organizations whether they may be a good place to work for a data engineer, three factors are top-of-mind: employer brand, team maturity, and team growth. We aggregated data from LinkedIn, Glassdoor, and Forbes Best Places to Work to help quantitatively substantiate these factors. 

This methodology leans towards larger more established companies. The dataset of potential companies was extensive but not comprehensive. Our methodology focuses on precision rather than recall. In other words, for our analysis, we only included companies where we have sufficient data.

Employer brand: estimated based on the Glassdoor company rating, and appearances on the Forbes Best Places to Work list in the past five years (2016 – 2021)

Team maturity: estimated based on the number of data engineers profiles on LinkedIn 

Team growth: historical growth is estimated based on the one-year growth of profiles with data engineering job titles on LinkedIn at a company, and forward-looking growth is estimated based on the open positions in data science on LinkedIn

Top companies to work for

Let’s look at companies that scored well in all three factors: employer brand, team maturity, and team growth. For this list, we focused on companies that scored at least 3.7 out of 5.0 on Glassdoor, which demonstrates they have a good employer brand. 

These companies also experienced positive team growth in data engineering over the past 12 months, currently operating a 20+ person data engineering team, and are continuing to grow, as observable with three or more open data engineering job postings on LinkedIn.


There you have it, the good companies to work for as a data engineering practitioner. This analysis focuses on larger, more established companies. There are also many earlier-stage companies that can be suitable for data engineers to take on leadership roles. When specific opportunities come up, you can use the three fundamental factors: employer brand, team maturity, and team size, to evaluate if they are right for you.

The book “How To Lead in Data Science”  talks about assessing the industry and the role in addition to assessing the company and the team. To learn how to advance your career in the data field to take on more responsibilities and amplify your impact, purchase a copy of the book. Use the 40% discount code podexchange20


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