Top Data Engineer Jobs in Dublin

Design, develop, and maintain scalable ELT data pipelines on Databricks/Palmos using Python, Spark/PySpark and SQL. Onboard enterprise datasets, apply CI/CD and testing, implement data governance and security, optimize performance, and support analytics and AI/ML use cases while leveraging enterprise AI-assisted development tools.

Design, build, test, and maintain scalable ELT/data pipelines and architectures using Python, Databricks, and AWS. Ensure data security and controls, update data models, and perform advanced SQL and statistical data analysis to support business analytics.

Build and operate scalable CI/CD pipelines, cloud infrastructure, and runtime environments to deploy and operate foundation models and data workloads. Automate deployments, ensure observability, embed security best practices, support Databricks, troubleshoot production issues, and promote DevOps/IaC practices.

Build and operate scalable cloud infrastructure and CI/CD pipelines to support foundation model training, deployment, and AI/data workloads. Implement IaC, container orchestration, monitoring, security best practices, Databricks integration, incident response, and automation to move models reliably from development to production.

Lead DevOps Engineer will build and operate cloud infrastructure and CI/CD pipelines to deploy and run foundation models and AI/data workloads. Responsibilities include infrastructure as code, container orchestration, monitoring, security best practices, Databricks support, incident response, and collaboration with AI, data, and software engineers to enable reliable, scalable production AI systems.

Build and operate scalable CI/CD pipelines, cloud infrastructure (primarily AWS), and runtime environments for foundation-model and data workloads. Automate deployments, manage IaC, container orchestration, Databricks integration, monitoring/observability, and secure pipelines. Collaborate with AI, data, and software engineers, troubleshoot production issues, and promote DevOps best practices for reliable model training and inference at scale.

Lead DevOps Engineer builds and operates scalable CI/CD pipelines, cloud infrastructure, and runtime environments for foundation model development and AI workloads. Responsibilities include infrastructure as code, container orchestration, model deployment (inference and batch), monitoring/alerting, security best practices, Databricks support, incident response, and collaboration with AI, data, and software engineers to automate and harden production AI systems.

Build, operate, and automate cloud infrastructure and CI/CD pipelines to enable foundation model development and production AI workloads. Manage model deployment (inference, batch, streaming), ensure observability, resilience, security (secrets management, IAM), and collaborate with AI, data, and software engineers to streamline development-to-production workflows and incident response.

Lead DevOps Engineer responsible for building and operating CI/CD pipelines, cloud infrastructure (primarily AWS), and runtime environments to deploy and operate foundation models and data workflows. Automate deployments, ensure observability, embed security best practices, support Databricks, troubleshoot production incidents, and promote DevOps/IaC practices to enable scalable, reliable AI systems.

Build and operate scalable CI/CD pipelines, cloud infrastructure, and runtime environments to deploy and operate foundation models and AI/data workloads; automate releases, ensure observability, security, and high availability; support Databricks, incident response, and cross-team collaboration to move AI solutions from development to production.

Build and operate cloud infrastructure and CI/CD pipelines for foundation-model and data workloads. Automate deployments, manage AWS-based environments, support model inference and batch jobs, implement observability and security best practices, and collaborate with AI/data teams on production readiness and incident response.

Build and operate scalable CI/CD pipelines, cloud infrastructure, and runtime environments to deploy and run foundation-model and data workloads. Automate deployments, manage AWS-based infrastructure, support Databricks workflows, implement observability and security best practices, troubleshoot production incidents, and collaborate with AI, data, and software teams to ensure reliable, secure AI model delivery.

Build and operate scalable CI/CD pipelines, cloud infrastructure, and runtime environments to support foundation model training, deployment, and production AI workloads. Automate releases, ensure observability, security, and high availability, support Databricks integrations, troubleshoot incidents, and promote DevOps best practices across AI and data teams.

Design, build, and maintain scalable ETL/ELT pipelines and data integrations to consolidate, transform, and prepare large datasets for analytics and ML. Enforce best practices, data governance, monitoring, and support while collaborating with business and analytics teams to improve data quality and enable insight-driven decisions.

The role involves developing capabilities for data parsing and enrichment in a cloud environment, leading cross-team initiatives, and ensuring high availability in distributed systems.

As a Senior Data Engineer, you'll develop and maintain ETL data pipelines, integrate data from multiple sources, ensure data quality, and support analytics and ML tasks. You'll also collaborate on architecture and best practices.

Maintain and enhance an Azure-based cloud data platform: monitor and re-run Azure Pipelines, troubleshoot pipeline failures and data reconciliation, perform impact analysis and performance tuning, support incidents/changes/projects, and assist auditors/governance on data queries.

Join an Agile data engineering team to migrate analytics from Oracle to Microsoft Fabric. Design and build data pipelines, metadata-driven ingestion and orchestration frameworks, perform integration testing and data mirroring, contribute to analytics data modelling, and support Microsoft Purview implementation for scanning, cataloguing, and governance.

As a Principal Data Engineer, you will lead the architecture and development of scalable data solutions, mentor engineers, and influence data practices across the business using Python, SQL, and modern data tools.

The Data Engineer will analyze data to drive business insights, create dashboards, and collaborate with teams to optimize product performance using SQL and visualization tools.

Build the companys first data stack and data products (internal analytics and customer-facing). Work across data engineering, devops, and software tasks; ship early features, improve developer experience, document proposals, and iterate based on feedback.

Design, build, and maintain large-scale data pipelines, lakehouse and analytics systems. Develop frameworks, SLAs, complex queries, and collaborate with engineers and data scientists to deliver data-driven solutions and prototypes.

Build and architect an in-house data warehouse and pipelines, enforce data governance and security, enable advanced analytics and reporting, and reduce reliance on external data providers to support credit and markets investment analytics.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

Design, build, and maintain ELT pipelines and dbt SQL models for analytics; optimize performance, scalability, and costs; apply AI-enabled tools to improve engineering workflows; collaborate to translate requirements into production-grade data assets.