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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Your opportunity to make a real impact and shape the future of financial services is waiting for you. Let's push the boundaries of what's possible together.
As a Senior Director of Software Engineering at JPMorganChase within the Commercial and Investment Bank Operations team, you lead multiple technical areas, manage the activities of multiple departments, and collaborate across Data and AI domains. Your expertise is applied cross-functionally to drive the adoption and implementation of technical methods within various teams and aid the firm in remaining at the forefront of industry trends, best practices, and technological advances.
Job Responsibilities
- Sets the direction for Data and AI technology platforms.
- Directly manages multiple areas with strategic focus on Transactional and Analytics Data and AI systems
- Sets and scales multi-department strategy for agentic AI-enabled engineering and SDLC/TLM automation (using enterprise-authorized tools within the work environment) to drive firmwide objectives (speed, scalability, reliability, and cost-to-serve), including portfolio-level standards for AI-orchestrated delivery workflows, release governance, automated test modernization, resilience engineering, and incident response acceleration; establishes guardrails for validation, security, resiliency, traceability, and reuse.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to drive cross-domain reuse and measurable capacity unlock outcomes across departments.
- Enable existing or new data applications and related data models to be ready for consumption by AI Agents
- Provides leadership and high-level direction to teams while frequently overseeing employee populations across multiple platforms, divisions, and lines of business
- Acts as the primary interface with senior leaders, stakeholders, and executives, driving consensus across competing objectives
- Manages multiple stakeholders, complex projects, and large cross-product collaborations
- Influences peer leaders and senior stakeholders across the business, product, and technology teams
Required qualifications, capabilities, and skills
- Bachelor's or Master's degree in Computer Science, Engineering, or related field
- 15+ years of experience in software engineering, with at least 5 years building and managing large scale, critical and complex distributed data management systems.
- Formal training or certification on software engineering concepts and 10+ years applied experience. In addition, 5+ years of experience leading technologists to manage, anticipate and solve complex technical items within your domain of expertise
- Experience developing or leading large or cross-functional teams of Data and AI technologists
- Demonstrated prior experience influencing across highly matrixed, complex organizations and delivering value at scale
- Experience leading multi-organization adoption of agentic AI-enabled engineering operating models (using enterprise-authorized tools within the work environment), including defining governance (human-in-the-loop decisioning, quality gates), measurement frameworks, and secure handling of sensitive inputs/outputs across teams.
- Deep understanding of responsible AI risk, controls, and resiliency/security expectations at scale, with demonstrated ability to advise senior leaders on safe adoption, portfolio governance, and reuse-first strategies.
- Proven track record of delivering complex trading or financial systems in a global banking environment
- Experience leading projects supporting complex data system design, data quality testing, and operational stability
- Ability to influence and drive change across technology and business teams
- Experience with hiring, developing, and recognizing talent
Preferred qualifications, capabilities, and skills
- Strong data engineering foundations including experience developing and managing complex data models, extensible ETL/ELT patterns and multi-modal Search techniques
- Proficiency with the modern data stack: SQL plus at least one general-purpose language (commonly Python/Java/Scala)
- Distributed systems & cloud literacy: understanding of performance, partitioning, storage formats, compute engines, and cloud services.
- Data quality & observability mindset: automated tests, monitoring/alerting, SLAs/SLOs, lineage, and incident response basics.
- Proven track record of delivering complex trading or financial systems in a global banking environment
- Experience with hiring, developing, and recognizing talent
This position is subject to Section 19 of the Federal Deposit Insurance Act. As such, an employment offer for this position is contingent on JPMorgan Chase's review of criminal conviction history, including pretrial diversions or program entries.
ABOUT USWe offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans
ABOUT THE TEAM
J.P. Morgan's Commercial & Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial & Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world.
Your opportunity to make a real impact and shape the future of financial services is waiting for you. Let's push the boundaries of what's possible together.
As a Senior Director of Software Engineering at JPMorganChase within the Commercial and Investment Bank Operations team, you lead multiple technical areas, manage the activities of multiple departments, and collaborate across Data and AI domains. Your expertise is applied cross-functionally to drive the adoption and implementation of technical methods within various teams and aid the firm in remaining at the forefront of industry trends, best practices, and technological advances.
Job Responsibilities
- Sets the direction for Data and AI technology platforms.
- Directly manages multiple areas with strategic focus on Transactional and Analytics Data and AI systems
- Sets and scales multi-department strategy for agentic AI-enabled engineering and SDLC/TLM automation (using enterprise-authorized tools within the work environment) to drive firmwide objectives (speed, scalability, reliability, and cost-to-serve), including portfolio-level standards for AI-orchestrated delivery workflows, release governance, automated test modernization, resilience engineering, and incident response acceleration; establishes guardrails for validation, security, resiliency, traceability, and reuse.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to drive cross-domain reuse and measurable capacity unlock outcomes across departments.
- Enable existing or new data applications and related data models to be ready for consumption by AI Agents
- Provides leadership and high-level direction to teams while frequently overseeing employee populations across multiple platforms, divisions, and lines of business
- Acts as the primary interface with senior leaders, stakeholders, and executives, driving consensus across competing objectives
- Manages multiple stakeholders, complex projects, and large cross-product collaborations
- Influences peer leaders and senior stakeholders across the business, product, and technology teams
Required qualifications, capabilities, and skills
- Bachelor's or Master's degree in Computer Science, Engineering, or related field
- 15+ years of experience in software engineering, with at least 5 years building and managing large scale, critical and complex distributed data management systems.
- Formal training or certification on software engineering concepts and 10+ years applied experience. In addition, 5+ years of experience leading technologists to manage, anticipate and solve complex technical items within your domain of expertise
- Experience developing or leading large or cross-functional teams of Data and AI technologists
- Demonstrated prior experience influencing across highly matrixed, complex organizations and delivering value at scale
- Experience leading multi-organization adoption of agentic AI-enabled engineering operating models (using enterprise-authorized tools within the work environment), including defining governance (human-in-the-loop decisioning, quality gates), measurement frameworks, and secure handling of sensitive inputs/outputs across teams.
- Deep understanding of responsible AI risk, controls, and resiliency/security expectations at scale, with demonstrated ability to advise senior leaders on safe adoption, portfolio governance, and reuse-first strategies.
- Proven track record of delivering complex trading or financial systems in a global banking environment
- Experience leading projects supporting complex data system design, data quality testing, and operational stability
- Ability to influence and drive change across technology and business teams
- Experience with hiring, developing, and recognizing talent
Preferred qualifications, capabilities, and skills
- Strong data engineering foundations including experience developing and managing complex data models, extensible ETL/ELT patterns and multi-modal Search techniques
- Proficiency with the modern data stack: SQL plus at least one general-purpose language (commonly Python/Java/Scala)
- Distributed systems & cloud literacy: understanding of performance, partitioning, storage formats, compute engines, and cloud services.
- Data quality & observability mindset: automated tests, monitoring/alerting, SLAs/SLOs, lineage, and incident response basics.
- Proven track record of delivering complex trading or financial systems in a global banking environment
- Experience with hiring, developing, and recognizing talent
This position is subject to Section 19 of the Federal Deposit Insurance Act. As such, an employment offer for this position is contingent on JPMorgan Chase's review of criminal conviction history, including pretrial diversions or program entries.
ABOUT USWe offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans
ABOUT THE TEAM
J.P. Morgan's Commercial & Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial & Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world.
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
- Amazon130
- Apple29
- Meta25
- LTM Limited15
- Google12
Top Industries Hiring
- Technology & Software92
- Retail24
- E-Commerce & Online Marketplaces21
- Consulting & Professional Services16
- Electronics & Hardware15
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 Virginia, based on current listings on Migrate Mate as of September 2026. Demand is especially concentrated in companies scaling their analytics or AI infrastructure.
How many data engineering manager jobs are remote?
About 66% of data engineering manager openings are fully remote or hybrid as of September 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 809+ Data Engineering Manager Jobs
Find roles that match your experience and apply in just a few clicks.
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