Top Tech Jobs & Startup Jobs in Dublin

Design, build, and operate enterprise-scale infrastructure platforms; write and review production code; own reliability, performance, security, and cost; run on-call rotations and respond to incidents; use AI-native engineering practices to accelerate development and automation; collaborate on technical decisions and decompose ambiguous problems into production-ready systems.

Lead a small senior team designing, building, and operating enterprise-scale infrastructure platforms. Own architecture through production, write and review production code, run on-call rotations, establish AI-native engineering practices, ensure security and reliability, decompose ambiguous problems, measure outcomes, and collaborate with stakeholders to drive scalable, cost-effective infrastructure solutions.

Lead creation of an AI-first engineering capability for database products: design reusable frameworks, ensure secure production code, guide AI-enabled delivery, set governance and guardrails, influence senior stakeholders, and mentor engineers to scale cross-product adoption.

Lead design and implementation of secure data management in public cloud. Perform risk-based assessments for Data & AI/ML services, review infrastructure-as-code, advise engineering teams on controls, integrate with security operations, and mentor junior staff to maintain robust cloud security.

Lead design and implement reliability, scalability, and observability for cloud and data platforms. Build IaC, CI/CD pipelines, use monitoring and SLO-driven improvements, apply enterprise AI for incident triage and reliability insights, and collaborate across teams to reduce toil and improve operational outcomes.

Lead and mentor SRE practices to design, implement, and operate reliable cloud and data platforms. Build IaC, CI/CD, observability, and containerized deployments; use enterprise AI to accelerate incident triage and reliability improvements while ensuring compliance and data sensitivity controls.

Lead design and build production software for enterprise-scale reliability: automation, control loops, self-healing, SLI/SLO definition and alerting, observability, incident leadership, on-call ownership, toil reduction, and AI-native engineering across the SDLC while ensuring security, performance, and cost control.

Lead and manage an engineering team to design, build, and operate scalable data integration and processing solutions using Databricks/Spark, AWS data services, Kafka streaming, and CI/CD tooling. Collaborate with cross-functional and distributed teams to deliver secure, high-performance data pipelines and analytics-ready platforms.

Lead and coach data engineering teams to onboard enterprise datasets into Palmos and deliver scalable Databricks/Spark pipelines. Drive adoption of data mesh and lakehouse patterns, enforce governance, security, observability, and SDLC best practices, and enable analytics and AI/ML use cases through high-quality, reusable data products.

Lead a product-aligned engineering organization to build and operate a cloud-native Talent Performance platform. Own technology direction, architecture modernization, integrations, delivery, reliability, security, and governance. Manage multiple cross-functional teams, vendor relationships, and stakeholder alignment. Drive adoption of AI-assisted engineering with appropriate controls while ensuring production readiness, observability, cost governance, and measurable engineering KPIs.