Top Python Jobs in Dublin

Lead Site Reliability Engineer responsible for application reliability, scalability, performance, observability, automation, incident response, capacity planning, and operational risk management. The role develops high-availability solutions, supports production readiness, troubleshoots complex issues, applies cloud and DevOps practices, improves monitoring and resilience, and mentors junior engineers.

Lead Site Reliability Engineer responsible for production readiness, high availability, scalability, observability, automation, incident response, capacity planning, and performance optimization. The role designs and maintains reliable cloud infrastructure, troubleshoots complex system issues, supports DevOps practices, manages operational risk and compliance, conducts post-mortems, and mentors junior engineers while improving reliability standards and developer ownership.

Lead Site Reliability Engineer responsible for application reliability, scalability, performance, and production readiness. Designs high-availability systems, automates operational and incident-response workflows, manages cloud infrastructure, monitors system health, performs troubleshooting and root-cause analysis, supports capacity planning, and drives proactive reliability improvements. The role also establishes operational standards, supports compliance and risk mitigation, conducts quality reviews, documents best practices, and mentors junior engineers.

Lead Site Reliability Engineer responsible for production readiness, high availability, scalability, observability, automation, incident response, capacity planning, and performance optimization. The role supports developers in building resilient products, manages operational risk and compliance, troubleshoots complex system issues, conducts post-mortems and quality reviews, maintains reliability standards, and mentors junior team members.

Lead Site Reliability Engineer responsible for ensuring application reliability, scalability, availability, and performance. The role designs high-availability systems, automates operational processes, manages observability and monitoring, troubleshoots incidents, conducts capacity planning, supports incident and change management, mitigates operational risks, and mentors junior engineers.

Lead Site Reliability Engineer responsible for ensuring application reliability, scalability, performance, and operational continuity. Designs high-availability systems, automates operational and incident-response processes, manages cloud infrastructure, monitors observability data, troubleshoots complex issues, performs capacity planning, supports incident and change management, and drives proactive reliability improvements. The role also establishes operational standards, supports production readiness, contributes to risk mitigation and compliance, and mentors junior engineers.

Lead site reliability engineering for Mastercard’s global applications and platforms. Ensure high availability, scalability, performance, observability, and operational continuity through automation, monitoring, capacity planning, incident response, troubleshooting, and risk management. Establish production-readiness standards, support developers with resilient software practices, conduct postmortems and quality reviews, and mentor junior engineers.

Lead reliability engineering for Mastercard’s production applications and platforms. Design high-availability, scalable systems; automate operational processes and incident response; monitor metrics, logs, and traces; troubleshoot complex issues; conduct capacity planning, performance optimization, and post-mortems; manage operational risk and compliance; establish reliability standards; and mentor junior engineers.

Lead reliability engineering for Mastercard applications and platforms by ensuring high availability, scalability, performance, and operational continuity. Design production-ready systems, automate operational and incident-response workflows, manage observability, capacity, monitoring, and cloud infrastructure, and troubleshoot complex issues. Apply DevOps and IT service-management practices, conduct post-mortems and quality reviews, mitigate operational risk, establish reliability standards, and mentor junior engineers.

Lead reliability engineering for Mastercard applications and platforms by implementing high-availability solutions, automation, observability, capacity planning, monitoring, and incident-response processes. Troubleshoot complex issues, conduct root-cause analysis and postmortems, manage operational risk and compliance, improve production readiness, and mentor junior engineers. Partner with developers throughout the lifecycle to build resilient, scalable, fault-tolerant products and maintain operational standards.

Lead site reliability engineering for Mastercard applications and platforms, ensuring high availability, scalability, performance, and operational continuity. Responsibilities include automation, scripting, observability, capacity planning, incident response, troubleshooting, root-cause analysis, change management, risk mitigation, compliance, production readiness, documentation, quality assurance, and mentoring junior engineers.

Monitor and respond to production alerts, troubleshoot distributed applications, improve reliability, and reduce recovery times. Support deployments and CI/CD pipelines across environments, implement automation, analyze ITSM activities, monitor service health, and participate in incident response and blameless postmortems. Collaborate with global development, operations, and product teams, identify resiliency gaps, optimize systems, and mentor junior team members while promoting DevOps standards and best practices.

Leads the design, development, deployment, and evaluation of machine learning solutions, including forecasting, recommendation, propensity, fraud, and customer analytics models. Defines experimentation frameworks, applies foundation-model embeddings, measures business impact, and partners with product, engineering, analytics, and business teams. Mentors data scientists and AI engineers, presents technical recommendations to leadership, supports hiring, and shapes the organization’s long-term technical direction.

Monitor and respond to production alerts, troubleshoot distributed web applications, improve reliability and system health, support deployments and CI/CD pipelines, automate operational processes, analyze ITSM activities, and lead incident response and blameless postmortems. Collaborate across development, operations, and product teams while mentoring junior resources and advancing DevOps standards and practices.

Leads the design, development, deployment, and evaluation of machine learning solutions, including predictive models, recommendation systems, forecasting, embeddings, and foundation-model applications. Defines experimentation frameworks and success metrics, measures business impact, partners with cross-functional stakeholders, presents findings to leadership, and mentors data scientists and AI engineers. The role also contributes to hiring, capability development, and the technical direction of the AI organization.

Leads the design and implementation of enterprise data platforms and pipelines supporting credit risk, decisioning, regulatory reporting, analytics, and AI initiatives. Architects Databricks, Spark, Delta Lake, and cloud-based solutions; establishes data quality, governance, lineage, security, monitoring, and operational standards; modernizes legacy systems; optimizes large-scale processing; influences technical strategy; mentors engineers; and partners with cross-functional stakeholders to deliver resilient, compliant data products.

Monitor and respond to production alerts, troubleshoot distributed applications, improve reliability and system health, support CI/CD deployments, automate operational processes, analyze ITSM activities, and conduct incident response and blameless postmortems. The role also collaborates globally across development, operations, and product teams while promoting DevOps standards and mentoring junior colleagues.

Lead the design and modernization of enterprise data platforms and pipelines supporting credit risk, decisioning, regulatory reporting, analytics, and AI initiatives. Architect cloud-based ETL/ELT, streaming, and lakehouse solutions using Databricks, Spark, Delta Lake, and related technologies. Establish data quality, governance, lineage, security, observability, and operational standards. Provide technical leadership, mentor engineers, influence architecture and roadmaps, optimize workloads, and collaborate across product, risk, analytics, architecture, and business teams.

Leads the design and evolution of enterprise data platforms and pipelines supporting credit risk, decisioning, regulatory reporting, analytics, and AI initiatives. Architects cloud-based ETL/ELT, lakehouse, streaming, governance, and data quality solutions; modernizes legacy assets; optimizes processing; and establishes engineering standards, testing, deployment, and operational practices. Partners across risk, product, architecture, and engineering, influences technical direction, mentors engineers, supports compliance, and drives strategic roadmap execution.

Leads the design, development, deployment, and evaluation of machine learning solutions, including predictive modeling, forecasting, recommendation systems, fraud detection, and customer analytics. Defines experimentation frameworks, applies foundation-model representations, measures business impact, and partners with cross-functional stakeholders. Mentors data scientists and AI engineers, presents recommendations to leadership, contributes to hiring, and helps shape the organization’s technical direction.

Monitor and respond to production alerts across web applications, troubleshoot distributed systems, and improve reliability and recovery times. Support deployments and CI/CD pipelines, automate operational processes, maintain service health, analyze ITSM activities, and provide feedback to development teams. The role also promotes DevOps standards, participates in incident response and blameless postmortems, mentors junior resources, and collaborates with global development, operations, and product teams.

Leads the design and implementation of enterprise data platforms and pipelines supporting credit risk, decisioning, analytics, regulatory reporting, and AI initiatives. Architects cloud-based lakehouse solutions using Databricks, Spark, Delta Lake, and related technologies; establishes data quality, governance, lineage, observability, and security controls; modernizes legacy systems; optimizes large-scale processing; and mentors engineers while influencing cross-functional technical strategy.

Leads the design, development, deployment, and evaluation of machine learning solutions, including predictive models, recommendation systems, forecasting, fraud detection, and customer analytics. Defines experimentation frameworks, applies foundation-model embeddings, measures business impact, partners with cross-functional stakeholders, presents technical recommendations, and mentors data scientists and AI engineers. The role also contributes to hiring, capability development, and the long-term technical direction of the AI organization.

Leads the design and delivery of enterprise data platforms and pipelines supporting credit risk, decisioning, analytics, regulatory reporting, and AI initiatives. Architects cloud-based lakehouse solutions using Databricks, Spark, Delta Lake, and modern ETL/ELT patterns. Establishes data quality, governance, lineage, observability, security, and compliance capabilities; modernizes legacy platforms; optimizes large-scale workloads; and drives engineering standards. Partners cross-functionally, influences architecture and roadmaps, mentors engineers, resolves complex dependencies, and supports technical hiring.

Leads the design, development, deployment, and evaluation of machine learning solutions, including predictive modeling, forecasting, recommendation systems, fraud detection, and customer analytics. Defines experimentation frameworks, applies embeddings and foundation models, measures business impact, partners with cross-functional stakeholders, presents technical recommendations, and mentors data scientists and AI engineers. Contributes to hiring, capability development, and the long-term technical direction of the AI organization.