Director of Data Engineering Jobs
Director of Data Engineering jobs are open across technology, financial services, healthcare, and retail, from senior individual contributor to VP-level leadership, with specializations in cloud data infrastructure, real-time streaming pipelines, and data platform architecture. Scan the live roles below and apply to whichever ones fit.
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Actian is the company behind the Data Intelligence Platform — an enterprise solution that helps organisations discover, govern, and trust their data at scale. Our platform connects metadata, data lineage, data quality, and business context into a single knowledge graph, enabling enterprises to turn raw data into reliable decisions. We operate globally, move fast, and build for the long term.
Software engineering is being redefined. The engineers who will shape the next decade of product development are not the ones who write the most code — they are the ones who know how to think clearly, ship confidently, and use every tool available to them, including AI, to multiply their impact.
The Actian Data Intelligence Platform helps teams find, understand, and use AI-ready data under clear governance. It connects metadata, data lineage, data quality, and business context into a single knowledge graph, so enterprises can turn raw data into reliable decisions — the trusted foundation that AI agents increasingly depend on to work. We operate globally, move fast, and build for the long term.
The Data Intelligence Platform has to deploy, operate, and pass audits in every one of the environments our customers run — public cloud, private cloud, and on-premises. We are standing up a dedicated platform team to make that possible, and we are hiring the leader who will build it.
This is a hands-on director role. You will stand up and lead a newly formed platform team, hire and grow it, and build alongside it from day one: you will design the platform services, write infrastructure code, and set the engineering standards the rest of engineering relies on.
We are an AI-first engineering organization, and in this role AI is how the work gets done. AI agents run operational tasks, AI accelerates the team's own engineering, and AI replaces tools we pay for today but no longer need. You will also build the infrastructure that runs the AI and ML workloads inside our product.
What You Will Own:
Platform services for the Data Intelligence Platform, including the infrastructure behind the data catalog, the knowledge graph, and the APIs that power customer integrations.
Cloud-agnostic deployment of the Platform across AWS, a second public cloud (Azure or Google Cloud), and customer on-premises and private-cloud environments — including packaging, installation, upgrades, and supportability.
The internal developer platform and CI/CD, including GitOps delivery, so engineering teams ship safely and quickly.
Security operations and compliance evidence for SOC 2 Type II, ISO 27001, and GDPR — including EU data residency — in partnership with the security team.
Cloud cost management and FinOps, with clear cost visibility and accountability across every environment.
Infrastructure for the AI and ML workloads used by the Platform.
Observability, reliability, and incident response for the services the team runs, including the team's on-call rotation.
Building and growing the team — hiring platform engineers, setting the operating model, and establishing the standards and rituals the team works by.
What You Will Deliver in Your First 12 Months:
High-functioning platform team: an operating model in place, and clear ownership boundaries with the product engineering teams you serve.
Portable, cloud-agnostic deployments: one deployment model that runs the Platform on AWS, on at least one other cloud, and on customer infrastructure — defined as code and delivered through GitOps.
AI agents running operations: agents that handle routine operational work — triage, remediation, change validation, and reporting — with measurable reductions in manual effort and a smaller tool footprint.
Platform foundations for the data catalog, the knowledge graph, and integration APIs: scalable, observable infrastructure that product teams build on.
Compliance automation and audit readiness: evidence for SOC 2 Type II, ISO 27001, and GDPR collected automatically from the platform rather than assembled by hand before each audit.
We expect this leader to treat AI as part of the platform. That means you will:
Design and run AI agents that operate infrastructure under clear guardrails — human approval for high-risk changes, and full audit trails.
Review our current tooling and retire tools that AI agents or simpler automation can replace, with a cost and risk case for each change.
Use AI coding and operations assistants in your own daily work, and set the standard for how the team uses them.
Build the compute, data, and access patterns that let AI and ML workloads run reliably in every environment we support.
What You Bring:
10+ years in platform engineering, infrastructure, DevOps, or SRE, including 3+ years managing engineers — with experience building or scaling a team, not only running an established one.
AI-first mindset, making extensive use of this technology for accelerating developments.
A track record of shipping on-premises or self-hosted software that customers install and run in their own environments.
Deep, hands-on experience with Kubernetes in production, infrastructure as code (Terraform/Terragrunt), and GitOps delivery with Argo CD or similar tooling.
Strong AWS experience, and the judgment to design for portability rather than lock-in to a single cloud.
Hands-on experience operating systems under SOC 2, ISO 27001, and GDPR controls, including data-residency requirements.
Experience managing cloud costs and making cost trade-offs explicit to engineering and business leaders.
Clear written communication, suited to a fully remote team across US and EU time zones.
Nice to Have:
Experience running infrastructure for data platforms, data catalogs, or graph databases.
Production experience on Azure or Google Cloud in addition to AWS.
Experience with air-gapped or restricted-network customer deployments.
Experience building infrastructure for AI and ML workloads, including model serving and vector or graph data stores.
Observability with OpenTelemetry or comparable tooling, from instrumentation through alerting.
Privileged access management with StrongDM or a comparable tool.
What We Value:
How You Will Work:
You will report to the SVP of Engineering and work closely with product engineering, security, customer success, and support. The role is fully remote from the US, UK, Spain, France or Germany, with at least four hours a day of overlap with both US Eastern Time and Central European Time. Expect occasional travel for team offsites and company-wide meetings.
What We Offer:
- The chance to be part of an innovative, fast-growing company making a significant impact in the data management space.
- Collaboration with a passionate and diverse team.
- Competitive salary and benefits package.
- Flexible work arrangements (remote or hybrid).
- Opportunities for professional growth and development.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Actian is the company behind the Data Intelligence Platform — an enterprise solution that helps organisations discover, govern, and trust their data at scale. Our platform connects metadata, data lineage, data quality, and business context into a single knowledge graph, enabling enterprises to turn raw data into reliable decisions. We operate globally, move fast, and build for the long term.
Software engineering is being redefined. The engineers who will shape the next decade of product development are not the ones who write the most code — they are the ones who know how to think clearly, ship confidently, and use every tool available to them, including AI, to multiply their impact.
The Actian Data Intelligence Platform helps teams find, understand, and use AI-ready data under clear governance. It connects metadata, data lineage, data quality, and business context into a single knowledge graph, so enterprises can turn raw data into reliable decisions — the trusted foundation that AI agents increasingly depend on to work. We operate globally, move fast, and build for the long term.
The Data Intelligence Platform has to deploy, operate, and pass audits in every one of the environments our customers run — public cloud, private cloud, and on-premises. We are standing up a dedicated platform team to make that possible, and we are hiring the leader who will build it.
This is a hands-on director role. You will stand up and lead a newly formed platform team, hire and grow it, and build alongside it from day one: you will design the platform services, write infrastructure code, and set the engineering standards the rest of engineering relies on.
We are an AI-first engineering organization, and in this role AI is how the work gets done. AI agents run operational tasks, AI accelerates the team's own engineering, and AI replaces tools we pay for today but no longer need. You will also build the infrastructure that runs the AI and ML workloads inside our product.
What You Will Own:
Platform services for the Data Intelligence Platform, including the infrastructure behind the data catalog, the knowledge graph, and the APIs that power customer integrations.
Cloud-agnostic deployment of the Platform across AWS, a second public cloud (Azure or Google Cloud), and customer on-premises and private-cloud environments — including packaging, installation, upgrades, and supportability.
The internal developer platform and CI/CD, including GitOps delivery, so engineering teams ship safely and quickly.
Security operations and compliance evidence for SOC 2 Type II, ISO 27001, and GDPR — including EU data residency — in partnership with the security team.
Cloud cost management and FinOps, with clear cost visibility and accountability across every environment.
Infrastructure for the AI and ML workloads used by the Platform.
Observability, reliability, and incident response for the services the team runs, including the team's on-call rotation.
Building and growing the team — hiring platform engineers, setting the operating model, and establishing the standards and rituals the team works by.
What You Will Deliver in Your First 12 Months:
High-functioning platform team: an operating model in place, and clear ownership boundaries with the product engineering teams you serve.
Portable, cloud-agnostic deployments: one deployment model that runs the Platform on AWS, on at least one other cloud, and on customer infrastructure — defined as code and delivered through GitOps.
AI agents running operations: agents that handle routine operational work — triage, remediation, change validation, and reporting — with measurable reductions in manual effort and a smaller tool footprint.
Platform foundations for the data catalog, the knowledge graph, and integration APIs: scalable, observable infrastructure that product teams build on.
Compliance automation and audit readiness: evidence for SOC 2 Type II, ISO 27001, and GDPR collected automatically from the platform rather than assembled by hand before each audit.
We expect this leader to treat AI as part of the platform. That means you will:
Design and run AI agents that operate infrastructure under clear guardrails — human approval for high-risk changes, and full audit trails.
Review our current tooling and retire tools that AI agents or simpler automation can replace, with a cost and risk case for each change.
Use AI coding and operations assistants in your own daily work, and set the standard for how the team uses them.
Build the compute, data, and access patterns that let AI and ML workloads run reliably in every environment we support.
What You Bring:
10+ years in platform engineering, infrastructure, DevOps, or SRE, including 3+ years managing engineers — with experience building or scaling a team, not only running an established one.
AI-first mindset, making extensive use of this technology for accelerating developments.
A track record of shipping on-premises or self-hosted software that customers install and run in their own environments.
Deep, hands-on experience with Kubernetes in production, infrastructure as code (Terraform/Terragrunt), and GitOps delivery with Argo CD or similar tooling.
Strong AWS experience, and the judgment to design for portability rather than lock-in to a single cloud.
Hands-on experience operating systems under SOC 2, ISO 27001, and GDPR controls, including data-residency requirements.
Experience managing cloud costs and making cost trade-offs explicit to engineering and business leaders.
Clear written communication, suited to a fully remote team across US and EU time zones.
Nice to Have:
Experience running infrastructure for data platforms, data catalogs, or graph databases.
Production experience on Azure or Google Cloud in addition to AWS.
Experience with air-gapped or restricted-network customer deployments.
Experience building infrastructure for AI and ML workloads, including model serving and vector or graph data stores.
Observability with OpenTelemetry or comparable tooling, from instrumentation through alerting.
Privileged access management with StrongDM or a comparable tool.
What We Value:
How You Will Work:
You will report to the SVP of Engineering and work closely with product engineering, security, customer success, and support. The role is fully remote from the US, UK, Spain, France or Germany, with at least four hours a day of overlap with both US Eastern Time and Central European Time. Expect occasional travel for team offsites and company-wide meetings.
What We Offer:
- The chance to be part of an innovative, fast-growing company making a significant impact in the data management space.
- Collaboration with a passionate and diverse team.
- Competitive salary and benefits package.
- Flexible work arrangements (remote or hybrid).
- Opportunities for professional growth and development.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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Find JobsDirector of Data Engineering Job Market
Who's Hiring
Top Industries Hiring
- Technology & Software
- Marketing & Advertising
- Healthcare & Medical Services
What Employers Look For
The qualifications that appear most often in director of data engineering jobs.
- 7+ years of data engineering experience with at least 2 years in a people management role
- Proficiency in distributed processing frameworks such as Apache Spark or Apache Flink
- Hands-on experience designing and operating cloud data platforms on AWS, GCP, or Azure
- Strong command of SQL and at least one programming language such as Python or Scala
- Experience with workflow orchestration tools such as Apache Airflow or Prefect
- Bachelor's or master's degree in computer science, engineering, or a related technical field
Tips for Your Director of Data Engineering Job Search
Quantify pipeline scale on your resume
Hiring managers for director of data engineering roles want to see data volume, latency targets, and team size. Replace vague language with specifics: petabytes processed, engineers managed, and cost reductions achieved through infrastructure changes.
Highlight cross-functional leadership experience
Directors own the relationship between data engineering and product, analytics, and machine learning teams. Your resume should show examples where you shaped roadmaps, resolved competing priorities, or drove alignment across those groups, not just technical delivery.
Apply early to roles that fit
Migrate Mate lists director of data engineering openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Tailor your portfolio to the stack listed
Job descriptions for director of data engineering roles name specific tools, whether Spark, dbt, Airflow, Kafka, or Databricks. Reorder your resume's technical section to surface the tools that match each posting before you apply.
Prepare a system design narrative for interviews
Expect at least one interview stage where you walk through a data platform you designed end to end. Prepare a clear story covering the business problem, your architectural decision-making, the tradeoffs you weighed, and the measurable outcome.
Negotiate scope alongside compensation
At the director level, budget ownership, headcount authority, and reporting structure are as negotiable as base pay. Ask explicitly about team size trajectory and whether the role owns infrastructure spend before you accept any offer.
Director of Data Engineering Jobs: Frequently Asked Questions
Which companies are hiring the most director of data engineerings?
The companies hiring the most director of data engineerings right now include JPMorganChase, AMD, and Hylan, with the largest share of openings in Texas, New York, and Washington, based on current listings on Migrate Mate as of November 2026. Demand is especially concentrated in technology, financial services, and healthcare organizations that are scaling their data platform capabilities.
How many director of data engineering jobs are remote?
About 86% of director of data engineering openings are fully remote or hybrid as of November 2026, reflecting strong employer flexibility at the senior leadership level. Roles focused on cloud-native data platform architecture and data mesh strategy tend to be the most remote-friendly, while positions requiring close coordination with on-site infrastructure teams are more likely to be hybrid or in-office.
How do you become a director of data engineering?
Most directors of data engineering start as individual contributor data engineers, progress into senior or staff-level roles where they own significant system design decisions, and then move into team lead or engineering manager positions before reaching the director level. Building a portfolio that demonstrates architectural ownership, cross-functional influence, and measurable impact on data platform reliability and cost is what typically accelerates that progression.
Can you get a director of data engineering role without prior director-level experience?
Yes, candidates who have been staff or principal data engineers, or who have led an engineering team as a manager, regularly make the move into director roles without having held the exact title before. The key is demonstrating that you have already been doing director-level work: owning a significant part of the data platform roadmap, managing engineers directly, and influencing decisions outside your immediate team.
What does the director of data engineering interview process look like?
The process typically includes an initial recruiter screen, a hiring manager conversation focused on leadership style and team philosophy, a technical system design round where you walk through a large-scale data platform architecture, and a cross-functional panel with stakeholders from product, analytics, or machine learning. A final conversation with a VP or CTO is common at this level, and some companies add a written or take-home strategy exercise.
Where can I find and apply to director of data engineering jobs?
You can find and apply to director of data engineering jobs on Migrate Mate, which lists current openings from across the United States. Search the roles available, find the ones that match your experience and target industry, and apply directly to each listing that fits.
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