Data Engineering Manager Jobs
Data Engineering Manager jobs are open across fintech, healthtech, e-commerce, and enterprise software, from senior individual contributor to director and VP, with specializations in cloud infrastructure, real-time pipelines, and platform engineering. Find a role that fits from the openings below and apply directly.
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As a databricks engineer, you will help design, build, and support data management solutions that enable reliable, scalable, and secure use of data across business and technology functions. In this role, you will collaborate with cross-functional stakeholders to develop data pipelines, improve data quality, and support data governance and integration efforts. The ideal candidate brings hands-on experience in data engineering and the ability to manage multiple priorities in a dynamic environment.
Work you'll do
As a US Delivery Center Senior Consultant, Data Engineering and Conversion Solutions on the team, you will:
- Design, build, test, and maintain data pipelines and data integration solutions
- Support data modeling, transformation, and storage processes across structured and unstructured data sources
- Monitor data workflows, troubleshoot issues, and implement fixes to improve reliability and performance
- Collaborate with business, analytics, and technology teams to define data requirements and support delivery priorities
- Apply data quality, metadata, and governance standards across data assets and processes
A successful candidate would possess these skills:
- Ability to work independently and collaborate as part of a team
- Effective written and verbal communication skills
- Meticulous attention to detail and quality of work product
- Ability to build and sustain professional relationships
- Ability to lead projects or workstreams
- Ability to manage and prioritize multiple tasks in a fast-paced and dynamic environment
- Strong interpersonal skills and professional demeanor
- Ability to meet deadlines
- Ability to provide clear guidance to others
The team
Deloitte's Government & Public Services (GPS) practice - our people, ideas, technology and outcomes - is designed for impact. Serving federal, state, & local government clients as well as public higher education institutions, our team of professionals brings fresh perspective to help clients anticipate disruption, reimagine the possible, and fulfill their mission promise.
Our AI & Data offering provides a full spectrum of solutions for designing, developing, and operating cutting-edge Data and AI platforms, products, insights, and services. Our offerings help clients innovate, enhance and operate their data, AI, and analytics capabilities, ensuring they can mature and scale effectively.
This opportunity sits within our Deloitte US Delivery Center model, which is dedicated to driving impactful business services. It leverages Deloitte's scale and talent, as well as a center delivery model to provide high-quality, cost-effective service with standardized processes and procedures to service businesses across Deloitte.
The Deloitte US Delivery Center has a small-business feel with a big-business impact. With the resources of Deloitte and a community feel, the delivery center model provides high-quality services to our clients. USDC professionals work out of one of our specific delivery center locations, and each location presents dynamic career opportunities for professionals to focus on their work with nominal travel requirements.
Qualifications
Required:
- Bachelor's degree required
- Must be able to obtain and maintain the required clearance for this role
- 2+ years of experience in data engineering, data integration, or data management, including 2+ years of experience with Structured Query Language (SQL)
- 2+ years of experience building or supporting extract, transform, and load (ETL) or extract, load, and transform (ELT) pipelines
- 2+ years of experience with Python and PySpark
- 1+ years of experience with Databricks and cloud-based data platforms, including data pipeline development and data transformation
- Ability to travel 10%, on average, based on the work you do and the clients and industries/sectors you serve
- Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future
Preferred:
- Experience with data warehousing concepts and dimensional data modeling
- Experience with data quality, metadata, or data governance processes
- Experience working with application programming interfaces (APIs) and system integrations
- Experience supporting Agile delivery teams
- Experience in a professional services environment
- Hybrid Work Model: Operate under a hybrid system requiring residence within a commutable distance to one of the US Delivery Center locations
- Co-location Expectation: Spend up to 30% of working time co-located at an assigned office for orchestrated opportunities, including projects, practice sessions, training, and Moments That Matter at a Deloitte Delivery Center location, Geo-Hub location, approved site, or project location
As a databricks engineer, you will help design, build, and support data management solutions that enable reliable, scalable, and secure use of data across business and technology functions. In this role, you will collaborate with cross-functional stakeholders to develop data pipelines, improve data quality, and support data governance and integration efforts. The ideal candidate brings hands-on experience in data engineering and the ability to manage multiple priorities in a dynamic environment.
Work you'll do
As a US Delivery Center Senior Consultant, Data Engineering and Conversion Solutions on the team, you will:
- Design, build, test, and maintain data pipelines and data integration solutions
- Support data modeling, transformation, and storage processes across structured and unstructured data sources
- Monitor data workflows, troubleshoot issues, and implement fixes to improve reliability and performance
- Collaborate with business, analytics, and technology teams to define data requirements and support delivery priorities
- Apply data quality, metadata, and governance standards across data assets and processes
A successful candidate would possess these skills:
- Ability to work independently and collaborate as part of a team
- Effective written and verbal communication skills
- Meticulous attention to detail and quality of work product
- Ability to build and sustain professional relationships
- Ability to lead projects or workstreams
- Ability to manage and prioritize multiple tasks in a fast-paced and dynamic environment
- Strong interpersonal skills and professional demeanor
- Ability to meet deadlines
- Ability to provide clear guidance to others
The team
Deloitte's Government & Public Services (GPS) practice - our people, ideas, technology and outcomes - is designed for impact. Serving federal, state, & local government clients as well as public higher education institutions, our team of professionals brings fresh perspective to help clients anticipate disruption, reimagine the possible, and fulfill their mission promise.
Our AI & Data offering provides a full spectrum of solutions for designing, developing, and operating cutting-edge Data and AI platforms, products, insights, and services. Our offerings help clients innovate, enhance and operate their data, AI, and analytics capabilities, ensuring they can mature and scale effectively.
This opportunity sits within our Deloitte US Delivery Center model, which is dedicated to driving impactful business services. It leverages Deloitte's scale and talent, as well as a center delivery model to provide high-quality, cost-effective service with standardized processes and procedures to service businesses across Deloitte.
The Deloitte US Delivery Center has a small-business feel with a big-business impact. With the resources of Deloitte and a community feel, the delivery center model provides high-quality services to our clients. USDC professionals work out of one of our specific delivery center locations, and each location presents dynamic career opportunities for professionals to focus on their work with nominal travel requirements.
Qualifications
Required:
- Bachelor's degree required
- Must be able to obtain and maintain the required clearance for this role
- 2+ years of experience in data engineering, data integration, or data management, including 2+ years of experience with Structured Query Language (SQL)
- 2+ years of experience building or supporting extract, transform, and load (ETL) or extract, load, and transform (ELT) pipelines
- 2+ years of experience with Python and PySpark
- 1+ years of experience with Databricks and cloud-based data platforms, including data pipeline development and data transformation
- Ability to travel 10%, on average, based on the work you do and the clients and industries/sectors you serve
- Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future
Preferred:
- Experience with data warehousing concepts and dimensional data modeling
- Experience with data quality, metadata, or data governance processes
- Experience working with application programming interfaces (APIs) and system integrations
- Experience supporting Agile delivery teams
- Experience in a professional services environment
- Hybrid Work Model: Operate under a hybrid system requiring residence within a commutable distance to one of the US Delivery Center locations
- Co-location Expectation: Spend up to 30% of working time co-located at an assigned office for orchestrated opportunities, including projects, practice sessions, training, and Moments That Matter at a Deloitte Delivery Center location, Geo-Hub location, approved site, or project location
Data Engineering Manager Jobs by Experience Level
Top Cities Hiring Data Engineering Managers
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Find Data Engineering Manager JobsData Engineering Manager Job Market
Who's Hiring
- Amazon125
- Apple30
- Meta27
- LTM Limited16
- Rosendin Electric12
Top Industries Hiring
- Technology & Software157
- Retail54
- E-Commerce & Online Marketplaces48
- Consulting & Professional Services28
- Electronics & Hardware24
What Employers Look For
The qualifications that appear most often in data engineering manager jobs.
- 5 or more years of experience in data engineering with at least 2 years in a management role
- Proficiency with cloud platforms such as AWS, GCP, or Azure and their managed data services
- Hands-on experience building and maintaining batch and streaming data pipelines at production scale
- Familiarity with modern data stack tools including dbt, Airflow, Spark, Kafka, or equivalent
- Experience with data warehousing solutions such as Snowflake, BigQuery, or Redshift
- Bachelor's degree in computer science, engineering, or a related technical field
Tips for Your Data Engineering Manager Job Search
Quantify pipeline scale on your resume
Hiring managers want to see the scope of what you've owned. Replace vague bullets with specifics: daily data volume processed, number of engineers managed, and latency improvements shipped. Data engineering manager resumes that lack those anchors read as generic.
Highlight architecture decisions, not just tools
Listings for this role screen for judgment, not just Spark or Kafka experience. Show where you chose one approach over another and why. Candidates who explain tradeoffs in their materials move faster through early screens than those who list tools alone.
Target listings that match your stack depth
Filter openings by the data platform your team has used most, whether that's Databricks, Snowflake, dbt, or a cloud-native stack. Applying where your hands-on depth aligns with the job description increases your callback rate more than applying broadly.
Apply early to roles that fit
Migrate Mate lists data engineering manager openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Prepare a cross-functional leadership story
Interviewers at this level probe how you work with data science, product, and platform teams, not just how you run your own squad. Have a concrete example ready where you aligned engineering priorities with a non-engineering stakeholder and delivered a measurable outcome.
Negotiate scope before negotiating anything else
In final-round conversations, clarify team size, on-call expectations, and technical debt load before discussing other terms. Data engineering manager roles vary widely in what 'manager' actually means, and misaligned scope is the top reason early attrition happens in this function.
Data Engineering Manager Jobs: Frequently Asked Questions
Which companies are hiring the most data engineering managers?
The companies hiring the most data engineering managers right now include Amazon, Apple, and Meta, with the largest share of openings in California, Texas, and Washington, based on current listings on Migrate Mate as of August 2026. Demand is especially concentrated in companies scaling their analytics or AI infrastructure.
How many data engineering manager jobs are remote?
About 57% of data engineering manager openings are fully remote or hybrid as of August 2026, making this one of the more remote-accessible management roles in engineering. Platform engineering and cloud infrastructure sub-areas tend to have the highest share of fully distributed positions.
How do you become a data engineering manager?
Most data engineering managers start as senior data engineers, take ownership of project delivery, then step into informal team-lead responsibilities before moving into a formal manager title. The clearest path is building both deep pipeline expertise and a record of mentoring junior engineers, then seeking a team lead or staff-level role where you can demonstrate cross-functional coordination and hiring involvement.
Can you get hired as a data engineering manager with limited management experience?
Yes, particularly at startups and growth-stage companies where the first data engineering manager hire is often a strong senior engineer willing to grow into the role. The most effective approach is to surface any informal leadership experience, such as onboarding engineers, owning architecture decisions, or running incident reviews, and frame those in your resume and interviews as evidence of managerial readiness.
What does the data engineering manager interview process look like?
Most processes include a recruiter screen, a technical assessment covering pipeline design and system architecture, a people-management interview focused on team structure and conflict resolution, and a cross-functional panel with data science or product stakeholders. Final rounds often include a presentation where you propose an engineering roadmap or critique an existing data architecture, which tests both technical judgment and communication.
Where can I find and apply to data engineering manager jobs?
You can find and apply to data engineering manager jobs on Migrate Mate, which lists current openings from across the United States. Find roles that match your experience and stack, then apply directly to each listing from the page.
See All 816+ Data Engineering Manager Jobs
Find roles that match your experience and apply in just a few clicks.
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