Top Data Engineer Jobs in Dublin

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.

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.

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.

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 Databricks data pipelines using PySpark, SQL and dbt. Develop ETL/ELT solutions, data models and reusable datasets, ensure production reliability and data quality, support migration from Teradata to Databricks, and collaborate with analytics, data science and business stakeholders to deliver trusted data products.

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 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.

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 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.

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.

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.

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.

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.

Operate and automate a cloud-native cybersecurity data platform for the EU Sovereign region. Manage day-to-day administration, capacity planning, SLIs/SLOs, observability, incident response, and IaC to maximize uptime, reduce toil, and ensure compliance with European data residency requirements.

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 and deliver Azure-based data lake and data warehouse solutions integrating IoT, CRM, billing and external sources. Build ETL pipelines (SSIS, ADF), optimize SQL schemas, implement Azure data security, support Data Science and Power BI reporting, and ensure scalable, highly available production deployments.

Design, develop, and maintain ELT/ETL Snowflake data warehouse solutions; implement data standards, CI/CD, monitoring, and production support; collaborate with product, business, and vendors; mentor junior developers and improve data engineering practices.

Own end-to-end AI-automated data platform migration projects (1-4 concurrently): scope, plan, execute, and hand off. Act as primary customer contact, configure Datafold's Migration Agent, partner with engineering on execution, and help refine delivery playbooks.

The Principal Data Engineer will improve data pipeline architecture, optimize data flows, and support data projects for various teams in the company.

Design, build, and operate large-scale data pipelines and a Snowflake-based warehouse/lakehouse using Airflow, DBT, and Python. Integrate diverse data sources, enforce data quality SLAs, participate in on-call rotations, drive architecture and roadmap decisions, mentor engineers, and collaborate with ML/product teams to enable AI-ready data products.

Lead mechanical due diligence and feasibility for data center sites and deals. Validate cooling topology and equipment strategy (air, liquid, hybrid), assess capacity, vendor lead times, and integration with budgets and schedules. Define mechanical readiness gates, deliver validated scope to Delivery, coordinate with stakeholders and vendors, and help build repeatable evaluation standards for AI/hyperscale infrastructure acquisitions.

Lead data integration, automation, and analytics for commercial teams: design data marts and architectures, build Airflow and Power Automate workflows, write SQL and Python pipelines, deliver BI reports (Power BI/Cognos), and run end-to-end data science projects to provide actionable business insights.