What Are the Responsibilities of a Data Engineer Manager?

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Rafael Timbó
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Rafael Timbó
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Chief Technology Officer
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Explore the crucial responsibilities of data engineer managers in the evolving data science field, plus the requisite skills, job description, and industry demand for this position.
Published on
May 27, 2022
Updated on
May 28, 2024

Data science is an exploding field. Most larger companies have made the digital-first transformation and actively rely on insights provided by data analysts or data scientists to make informed, data-driven business decisions. This increased reliance on data has skyrocketed demand for data-related professionals. Data analysts need a reliable source of high-quality data to do their jobs and rely on data engineers to provide it.

In the data science hierarchy of needs described by Monica Rogati, data engineers work at levels 2 and 3, creating reliable ways to move, store, explore, and transform data so it’s usable when it reaches data scientists. As the first level, collecting, continues to expand, it increases the need for data engineers and data engineering managers.  

What Does a Data Engineer Do?

Before understanding the role of a data engineering manager, you must understand what a data engineer does. Data engineering involves designing, building, and maintaining systems for collecting, storing, transforming, and analyzing data. Although data analyst and data scientist careers are more well-known, these professionals rely on the work of data engineers to do their jobs.

Data engineers may perform the following tasks:

  • Collect datasets that are valuable to their companies
  • Build, test, and maintain data pipeline architecture using automation and scripting for repetitive tasks
  • Comply with data governance and data security regulations and policies
  • Develop algorithms to transform data into usable forms
  • Employ software engineering methodologies, including Agile, DevOps, architecture design, and service-oriented architecture
  • Adhere to data integrity practices guided by overall cyber security standards

Data engineers typically use the following languages, frameworks, and tools:

  • Popular programming languages like R, Python, Java, SQL, and Scala
  • Extract, transform, and load (ETL) systems
  • Open frameworks like Apache Spark, Hadoop, and Kafka
  • Pandas, a Python library used for manipulating data
  • Data visualizations and dashboards
  • Cloud platforms like AWS, Google Cloud Data Engineering, and Microsoft Azure

Data Management vs. Data Engineering

Data engineering and data management are closely related concepts that work together. Though they're often lumped together, there are some differences between them.  

Data Management

Data management is the process of acquiring, storing, processing, and applying data. It's divided into three subsets: metadata management, data quality management, and data security management.

Metadata Management

Metadata can be technical, operational, or business-oriented. It plays a key role in finding and using data.

Data Quality Management

Not all data is good data. You can't trust the insights it provides if you're using inaccurate, out-of-date, or corrupted data. Data quality management is concerned with assessing and managing data integrity, timeliness, accuracy, and consistency.

Data Security Management

Data security is tightly regulated in many industries. Data security management protects data access, use, release, and destruction processes. Some of the most important features in data security management include:

  • Only allowing authorized users to access data
  • Recording all access and modifications to sensitive data
  • Deleting confidential or sensitive data that are no longer in use
  • Protecting sensitive data through tokenization or encryption

Data Engineering

While data management is primarily concerned with how data is handled to ensure its quality, security, and indexability, data engineering involves designing and building the architecture to collect, transform, store, and serve data for efficient use by data scientists. Data engineers:

  • Build data pipelines
  • Analyze and organize data
  • Build data ingestion systems
  • Perform quality checks on data

Data Engineer Manager Responsibilities

Data engineering managers must stay on top of emerging trends and technologies in data engineering and supervise the data engineers who work for them. They need a broad range of technical and interpersonal skills to do their jobs. Here are some common data engineering manager roles and responsibilities.

  1. Create a Modern Data Stack (MDS)

The MDS is a new approach to data integration, but it's exploding in popularity. Compared with legacy data stacks, an MDS saves time and frees data engineers and analysts to focus on accomplishing high-value data tasks rather than setting up and maintaining on-site systems. An MDS consists of a suite of largely off-the-shelf tools used at different steps along the data flow.

Used together, these tools allow organizations to become fundamentally data-driven. Because this is a rapidly evolving concept with little established protocol, a big part of a data engineering manager's job will be evaluating and implementing an MDS.

There's no one-stop solution for an MDS, so every organization's solution will look different. However, there are three basic features of any effective MDS:

  • It can be easily used by people with a wide range of skills and abilities, particularly "non-techy" people
  • It's agile, so data is readily available in a lake or warehouse and elastic enough to allow auto-scale processing based on need
  • It's cloud-native, which allows it to be fast, flexible, and provided on a pay-as-you-go basis

Components of an MDS include:

  • A managed ETL data pipeline
  • A cloud-based storage system for raw data
  • A tool for data transformation
  • A data visualization platform

The MDS follows the general technological trend of moving away from local systems and toward cloud-based systems. It lowers the technical barrier to entry by promoting end-user accessibility and scalability. An MDS saves time, money, and effort compared to legacy systems.

  1. Select the Right Tools

Along with creating a modern data stack, data engineering managers are responsible for selecting the right tools. There are numerous tools available for each component of an MDS. Choosing the right ones will depend on factors related to the company's resources and the skills and knowledge of the data engineering department. Some considerations a data engineering manager will take into account when choosing tools include:

  • The type of data being processed
  • The language their team is most familiar with
  • The budget
  • The existing tools being used

Data engineering technology is constantly evolving and updating, so selecting the correct software development tools is an ongoing part of the job. Many of the standard tools used today were unknown just a few years ago. Data engineering managers have to keep up with new technologies as they're released and decide when the benefits of changing tools outweigh the drawbacks.  

When it is time to make a change, they'll design and oversee the onboarding process for all new users. This may include training data engineers in the use of new technologies and ensuring that end users outside of the data department can use them.

  1. Structure an Effective Team

Data engineering managers must know as much about people as they do about data. They need to understand the strengths and weaknesses of engineers and how they approach problems through a typical development team structure. With tech skills becoming outdated and changing so quickly, managers will increasingly have to consider solutions such as upskilling and reskilling current team members rather than continually hiring new data engineers.

  1. Communicate With Business Users

One of the main benefits of an MDS is that it's easy for non-engineers to use. Business users should be able to use it intuitively to access the data they need to analyze for strategic planning. However, this doesn't mean they won't need training or help along the way. Data engineering managers will have to work closely with business users to educate them on achieving their end goals and the benefits of their department's work.

To effectively communicate with business users, data engineering managers need to be able to explain complicated technical concepts in simple ways. While "tech talk" is second nature to engineers, many business users will be lost if they throw around obscure data science terms.

  1. Hire Junior Data Engineers

Data engineering managers hire and mentor junior engineers as they grow into more senior roles. Based on software developer career paths, identifying promising talent is an essential responsibility for managers. They are also responsible for hiring and overseeing senior data engineers as they train junior engineers. Interviewing candidates is a large part of this responsibility. Given the shortage of skilled tech workers, filling empty positions is an ongoing challenge.

When hiring for junior positions, managers should look for candidates who:

  • Can learn quickly
  • Expect learning new skills to be a career-long process
  • Take the initiative in doing a job and seeking help when needed
  • Have well-developed problem-solving abilities

Finding engineers with the requisite hard skills may be difficult, but using appropriate screening tools can help. The more significant challenge will likely be finding skilled data engineers with the proper soft skills who fit the existing team well.

Creating a cohesive team involves understanding how different personalities work together to achieve results. Additionally, ensure junior engineers are open to constructive feedback as they progress, allowing managers to direct their employees more efficiently.

Hiring a candidate with skills and personality traits that enhance, or possibly counter, those on the existing team is often as important as hiring one with rock-star machine learning skills. 

  1. Facilitate Interdepartmental Communication

When software development, product, and data teams become siloed, it can negatively affect data quality and lead to technical debt. Data engineering managers need to work with other teams to identify issues and be proactive about changes that can help avoid data breaking. They also work closely with data scientists to determine their data needs for particular projects and design solutions for them.

Interdepartmental communication is crucial as upstream changes to the data model may affect the downstream data resources. This problem becomes more of an issue with scale since the model may change more often. Implementing a transparent communication method that all departments can update and access can mitigate some of the problems associated with changing data models.

Data Engineer Manager Job Description

Typically, a standard job description includes the job title, department, direct supervisor, and primary responsibilities of the position. It’s also helpful to include an overview of benefits for the hiring organization to both entice and inform applicants about expected healthcare, PTO, and compensation benefits. The job description for a data engineer manager will naturally vary based on the industry and company. Beginning with the job title, a typical data engineer manager job description should resemble the following:

Job title: Data Engineer Manager

Department: Information Technology

Supervisor: Chief Technical Officer

Job Overview: As a data engineer manager, you will lead a team of data engineers in designing, building, and maintaining scalable and robust data pipelines. Your role will involve strategic planning, team management, and hands-on technical work to ensure the effective processing and analysis of large datasets. You will collaborate with other departments to support data-driven decision-making and contribute to the company's overall data strategy.

Key Responsibilities:

  • Working with software engineers, product managers, and data scientists to build data architecture that drives insights
  • Creating, defining, and executing a plan for business intelligence and data warehousing across a product vertical
  • Designing and scaling a data engineering team 
  • Managing data warehouse plans across a product vertical
  • Delivering intuitive, high-impact data dashboards and visualizations
  • Leading the delivery of scalable data and analytics solutions
  • Implementing new and open-source technologies
  • Designing and building reusable components, frameworks, and libraries at scale to support analytics data products
  • Improving processes regarding data flow and quality to improve data accuracy, viability, and value

Position Requirements:

  • Bachelor's degree in computer science, math, physics, or other technical fields
  • Fundamental data architecture and design skills
  • 3 to 5 years of technical management experience
  • Expert knowledge of SQL
  • Experience building scalable data solutions
  • 6 to 10 years of solution development and technology experience  
  • Strong knowledge of various data engines

Company Benefits:

  • Industry-competitive salary that includes a yearly cost-of-living adjustment
  • Full medical, dental, and vision coverage
  • 401k with company matching
  • Unlimited PTO with flex work options

Senior Data Engineer Manager Interview Process

While every company will have its own process, data engineering managers generally go through several technical and leadership interviews, starting with broad screenings and working their way up the organizational structure until the final interviews. Here's an example of what the process might look like.

Leadership Screening Interview

This situational and behavioral interview with the hiring manager is designed to gauge leadership skills and potential. It will include questions like:

  • Tell me about the most challenging decision you've ever made
  • What does a good data engineering team look like?
  • Share a time when a project dramatically shifted directions at the last minute, including how you handled it

In addition to determining leadership style and effectiveness, questions like this are a good way to judge a candidate's ability to communicate concisely. Comprehensive answers should include information from the STAR model:

  • Situation: Set the scene and give the details surrounding it
  • Task: Describe tasks and responsibilities in the example
  • Action: Explain how the issue was handled
  • Results: Describe the specific outcome that followed from the action

Technical Screening Test

This coding exercise will likely use SQL and a common programming language like Java, Python, or C. Candidates are asked to provide solutions to more complex problems than at the data engineering level. LeetCode is a good resource for finding these types of questions. However, a manager's approach to problems is more important than the actual coding.

Technical Data Exercise

Once they pass the screener interviews, the candidate will move on to the next round, which may be on-site, depending on the company. This is a longer technical interview where they are given a problem or feature and asked to explain how they would solve the problem or implement the feature. After discussing their options, they'll be asked to code the solutions.

They may also be asked a technical data question about a business or app idea and what metrics would be best to collect to gather data for it and then design a schema for a database. They should then be able to write queries to retrieve the metrics.

Leadership and Stakeholder Interviews

Finally, the candidate will likely have one or two more interviews to assess their leadership potential and ability to work with other teams. Again, their answers should be comprehensive and include information using the STAR format.

Learn More: Data Warehouse as a Service: How Does it Work?

Data Engineer Manager Interview Questions

Hiring managers seek data engineering managers who prioritize work, mentor data engineers, and proactively manage risks. Some questions they may ask to determine an applicant's abilities to do this include:

  • Given these tasks, how would you prioritize the work?
  • How do you plan projects to keep them on schedule?
  • What do you do when they start running behind schedule?
  • How do you keep your team's skills updated?
  • How do you divide work among your team members?
  • How do you resolve the conflict when you and another team leader disagree about task prioritization?
  • What soft skills did it take for you to get where you are?

Hire Data Engineer Managers With Revelo

If you're hiring for a data engineering manager role or other highly sought-after tech talent positions, consider working with a tech talent marketplace like Revelo. Revelo has a talent base of pre-vetted developers with the expertise and experience to impact your organization immediately. 

We take the hassle out of finding skilled tech workers so you can focus on growing your business—after hiring, Revelo provides ongoing administrative support with payroll, taxes, and local compliance.

Contact us today to hire data engineer managers and optimize your organization’s data pipeline.

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