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United Kingdom
Remote only
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.
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.
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.
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.
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.
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.
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.
