Top Data Engineer Jobs in Dublin

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.

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.

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.

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.

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.

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.

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.

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.

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 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, 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 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 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 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 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 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 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.

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 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.

Lead design and build of enterprise-scale cybersecurity data lakehouse ingestion, enrichment, and semantic layers. Scale pipelines from Splunk to cloud-native lakehouse, implement data observability and reliability, enable AI/advanced analytics, and mentor engineers to raise engineering maturity and modernize practices.