Top Data Engineer Jobs in Dublin

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.

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.

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.

Design, build, and maintain ETL/ELT data pipelines and processing jobs (PySpark/SQL) on cloud platforms (Azure, Databricks, Snowflake). Ensure data quality, governance, monitoring, and automation; partner with analytics and infrastructure teams to productionalize analytic pipelines and support platform architecture.

Design, build, and maintain scalable ETL/ELT pipelines to migrate and integrate consumer healthcare data into cloud data lakes/warehouses. Clean and transform datasets for analytics and ML, enforce governance and security, develop reusable frameworks, monitor system reliability, and collaborate with cross-functional analytics and business teams to improve predictive models and lower healthcare costs.

The Data Engineer will translate business objectives into actionable analytics, develop Big Data applications, automate quality checks, and collaborate on data privacy compliance.

Build and maintain BigQuery data models and ETL pipelines using Dataform and Python; implement data quality and observability; schedule workflows with Cloud Composer/Airflow; support Looker reporting and ML feature pipelines; collaborate with Data Science and Product to productionize analytics.

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.

Lead design, build, and operate scalable data pipelines and products to support foundation models and AI workloads. Own ingestion, transformation, feature engineering enablement, data quality, governance, and production readiness while mentoring engineers and ensuring enterprise standards.

Lead design, build, and operate scalable, governed data pipelines and products for AI model training, inference, and evaluation. Partner with AI engineers, ensure data quality, lineage, governance, and performance; mentor engineers and drive delivery.

Lead design, build, and operate scalable data pipelines and products to support foundation model training, inference, and evaluation. Ensure data quality, lineage, governance, and performance across batch and streaming workloads; enable feature engineering and feature stores; mentor engineers and drive delivery with platform and product teams.

Lead design, build, and operate scalable data pipelines and data products for foundation-model training, inference, and evaluation. Ensure data quality, lineage, governance, and performance; enable feature engineering and training datasets; collaborate with AI, platform, and product teams; mentor engineers and oversee delivery.

Lead design, build, and operate scalable data pipelines and products for AI foundation models. Own ingestion, batch and streaming transforms, feature engineering and training datasets, ensure data quality, lineage, governance, performance tuning, and secure access. Collaborate with AI, platform, and product teams, lead technical reviews, mentor engineers, and contribute to program planning and delivery.

Lead design, build, and operate scalable data pipelines and products to support foundation model training, inference, and evaluation. Own ingestion, transformation, streaming and batch patterns, feature engineering support, data quality, lineage, governance, and performance optimization. Collaborate with AI, platform, and product teams, lead technical reviews, mentor engineers, and contribute to program planning and delivery oversight.

Lead design, build, and operate scalable data pipelines and products for foundation model training, inference, and evaluation. Own ingestion, transformation, batch/stream processing, feature engineering, data quality, lineage, governance, and performance tuning. Collaborate with AI, platform, and product teams to ensure production readiness, security, and observability. Provide technical leadership, code/design reviews, mentoring, and delivery planning across the program.

Lead design, build, and operate scalable data pipelines and products for AI model training, inference, and experimentation. Ensure data quality, lineage, governance, and performance; enable feature engineering and feature stores; collaborate with AI, platform, and product teams; mentor engineers and oversee delivery.

Lead design, build, and operate scalable, governed data pipelines and products for foundation models and AI workloads. Partner with AI and platform teams to enable feature engineering, training/inference datasets, data quality, lineage, and secure access. Provide technical leadership, code/design reviews, mentoring, and delivery planning across cloud-native distributed data environments.

Lead design, build, and operate scalable data pipelines and products to support foundation model training, inference, and evaluation. Ensure data quality, governance, lineage, and secure access. Partner with AI, platform, and product teams, drive best practices, conduct reviews, mentor engineers, and manage delivery risks for AI data needs.

Lead design, build, and operate scalable data pipelines and data products to support foundation model training, inference, and evaluation. Partner with AI and platform teams to enable feature engineering, ensure data quality, governance, lineage, and secure access. Drive best practices for modeling, partitioning, performance, monitoring, and CI/CD. Provide technical leadership, code/design reviews, mentoring, and program-level planning to ensure production-ready, reliable data for AI workloads.

Design, build, and operate enterprise reporting and analytics platforms. Create scalable ELT/ETL pipelines, master data foundations, and lakehouse/warehouse semantic models. Deliver Power BI datasets and dashboards, implement data quality, governance, DevOps practices, and cost-management data solutions. Partner with finance and stakeholders and provide technical leadership across architecture, deployment, and run-state support.

The Data Engineer will design and maintain scalable data pipelines, optimize data infrastructure, monitor data quality, and enable analytics capabilities, ensuring alignment with business goals.

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.

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 Data Engineer, you will develop and manage data pipelines, ensuring reliable data ingestion and delivery while optimizing for simplicity and performance.

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.