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.

Lead hands-on design and delivery of scalable cloud-based data platforms and Lakehouse solutions, build high-volume batch and streaming pipelines, define standards for data modeling, quality, lineage, security, and observability, create reusable patterns, mentor engineers, and partner with product, AI, security, and governance stakeholders to enable analytics and AI use cases.

Build and maintain scalable ETL pipelines, integrate data from databases, APIs, streaming platforms, and external providers, and transform large datasets for analytics and machine learning. Establish reusable engineering frameworks, data governance, monitoring, alerting, and reliability practices. Collaborate with analytics and business teams on cloud data migration, predictive model enablement, and healthcare cost reduction. Required skills include SQL, Python, Scala or Java, cloud platforms, Snowflake or Databricks, DevOps, Git, and CI/CD.

Leads end-to-end software engineering for analytics platforms and applications, including architecture, development, testing, deployment, support, and technical direction. Builds scalable AI-powered solutions and large-scale data pipelines with Python, PySpark, and SQL. Evaluates emerging AI tools, maintains GitHub Actions CI/CD, supports cloud development, conducts reviews, creates prototypes, documents systems, and partners with product, architecture, data science, and engineering teams.

Lead design and delivery of shared observability data platform capabilities to reduce developer friction, improve operational visibility, enable cost intelligence, build self-service operational tooling, drive reusable services, mentor engineers, influence cross-team architecture, and participate in on-call to improve platform reliability.

Develop and maintain complex data pipelines and data sources using Snowflake, Airflow, Azure, SQL, and Python. Integrate on-premises and cloud data, build transformation routines, data models, stored procedures, masking solutions, monitoring tools, and automated data quality controls. Create CI/CD pipelines with GitHub Actions and collaborate with architects, analysts, and data stewards to deliver reliable analytics foundations. The role also supports scalable payment integrity data solutions and requires knowledge of data warehousing, integration, and security practices.

Lead architecture and development of backend services, integrations, internal tools, and data systems for engineering metrics, tooling spend, ROI, and cost reporting. Build reliable APIs and auditable analytics products, replace manual processes, own testing, monitoring, and production support, and mentor engineers. Collaborate with product and business stakeholders while applying agentic engineering practices.

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.

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

Lead architecture and implementation of a scalable data lakehouse and streaming platform. Define technical strategy, build resilient systems, optimize performance, mentor engineers, and partner across teams to enable analytics and ML use cases using modern data platform technologies.

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.

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 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.

Support AI and machine learning initiatives through data collection, cleaning, transformation, analysis, ETL pipeline development, feature engineering, basic model development, dashboards, reporting, documentation, and SQL and cloud learning under senior mentorship.

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.

Designs, builds, and maintains scalable Azure data pipelines and platform architecture. Responsibilities include developing Synapse pipelines, creating data models, optimizing databases, deploying through Azure DevOps and Terraform, implementing data quality and monitoring controls, supporting governance and security, troubleshooting environments, and sharing platform knowledge. The role also requires collaboration with analysts, infrastructure teams, technical stakeholders, and users while keeping current with Microsoft Fabric and migration approaches.

The Software Engineer for Data Infrastructure & Acquisition will enhance data collection for AI model training, improve ingestion pipelines, and collaborate with AI scientists for optimal data usage.

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.

Lead complex data analyses and data science projects using big data technologies. Develop systems, applications, and visual dashboards; assess system usage; improve data quality and processes; and build tools that address security threats and improve network reliability. Communicate technical findings to nontechnical audiences and guide peers through analytical methods, technologies, and system usage.

Designs, develops, and optimizes high-volume data pipelines in Databricks and Spark environments. The role integrates data from enterprise systems, collaborates with architects and data scientists, incorporates forecasting algorithms, optimizes Spark performance and cluster costs, follows Agile Scrum practices, mentors junior engineers, and maintains technical documentation.

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.