Top Data Engineer Jobs in Dublin

Leads the design and delivery of scalable data platforms and products for healthcare applications. Establishes standards for data modeling, quality, lineage, observability, testing, security, and operational readiness. Partners with product, AI, architecture, governance, and engineering teams to create technical roadmaps, reusable patterns, and multitenant architectures. Enables generative AI and conversational analytics solutions while mentoring engineers and evaluating architectural trade-offs.

Design, build, test, and maintain scalable data engineering solutions, including automated releases, ETL processes, data models, real-time pipelines, monitoring, legacy migrations, and security strategies. Transform raw data using business logic, support machine learning and business intelligence workloads, and produce technical documentation. The role requires extensive experience with cloud platforms, SQL, databases, data warehousing, Spark, Databricks, Airflow, Kubernetes, Docker, Python, and related data technologies.

Build and operate production data pipelines supporting analytics, AI, and agentic workflows. Responsibilities include implementing canonical data models, maintaining Databricks or Snowflake platforms, monitoring reliability, responding to incidents, validating healthcare data mappings, improving performance and cost, and documenting architecture. Requires strong SQL and Python skills, cloud data platform experience, ETL/ELT orchestration expertise, and healthcare or pharmaceutical data experience.

Build and evolve Toast’s cloud-native data platform, working hands-on with Spark, Iceberg, streaming infrastructure, lakehouse architecture, APIs, and feature-serving systems. Drive technical strategy and architectural decisions, influence engineering and product stakeholders, mentor engineers, and enable teams to build scalable data and AI/ML experiences. The role is hybrid in Dublin and supports major platform modernization initiatives.

Leads the design and evolution of enterprise data platforms and pipelines supporting credit risk, decisioning, regulatory reporting, analytics, and AI initiatives. Architects cloud-based ETL/ELT, lakehouse, streaming, governance, and data quality solutions; modernizes legacy assets; optimizes processing; and establishes engineering standards, testing, deployment, and operational practices. Partners across risk, product, architecture, and engineering, influences technical direction, mentors engineers, supports compliance, and drives strategic roadmap execution.

Leads the design and implementation of enterprise data platforms and pipelines supporting credit risk, decisioning, regulatory reporting, analytics, and AI initiatives. Architects Databricks, Spark, Delta Lake, and cloud-based solutions; establishes data quality, governance, lineage, security, monitoring, and operational standards; modernizes legacy systems; optimizes large-scale processing; influences technical strategy; mentors engineers; and partners with cross-functional stakeholders to deliver resilient, compliant data products.

Leads the design, development, and modernization of enterprise data platforms and pipelines supporting credit risk, analytics, regulatory reporting, and AI initiatives. Architects cloud-based ETL/ELT and streaming solutions using Databricks, Spark, Delta Lake, and related technologies. Establishes data quality, governance, lineage, security, monitoring, and operational standards while optimizing performance and cost. Partners with cross-functional stakeholders, influences architecture and roadmap decisions, mentors engineers, and drives delivery across complex initiatives.

Leads the design and modernization of enterprise data platforms and pipelines supporting credit risk, decisioning, regulatory reporting, analytics, and AI. Architects Databricks, Spark, Delta Lake, and cloud-based solutions; establishes governance, quality, lineage, observability, and security controls; optimizes large-scale processing; and guides technical strategy. Partners across risk, product, architecture, and engineering, mentors teams, influences stakeholders, supports compliance, and drives delivery across complex initiatives.

Leads the design and modernization of enterprise data platforms and pipelines supporting credit risk, decisioning, regulatory reporting, analytics, and AI initiatives. Architects cloud-based lakehouse solutions using Databricks, Spark, Delta Lake, and related technologies; establishes governance, quality, lineage, observability, security, and compliance controls; optimizes large-scale workloads; and drives technical standards, cross-functional delivery, mentoring, and strategic roadmap planning.

Lead the design and evolution of enterprise data platforms and pipelines supporting credit risk, decisioning, regulatory reporting, analytics, and AI initiatives. Architect cloud-based ETL and ELT solutions using Databricks, Spark, Delta Lake, and related technologies. Establish data quality, governance, lineage, observability, security, and compliance controls. Drive modernization, performance optimization, technical standards, cross-functional delivery, architectural decisions, mentoring, talent assessment, and strategic roadmap planning.

Leads the design and modernization of enterprise data platforms and pipelines supporting credit risk, decisioning, regulatory reporting, analytics, and AI initiatives. Builds scalable cloud-based ETL/ELT and streaming solutions using Databricks, Spark, Delta Lake, SQL, and Python. Establishes data quality, governance, lineage, security, monitoring, and operational excellence practices. Partners across risk, product, architecture, and engineering; influences technical direction, mentors engineers, supports compliance, and drives strategic roadmap execution.

Leads the design and delivery of enterprise data platforms and pipelines supporting credit risk, decisioning, analytics, regulatory reporting, and AI initiatives. Architects cloud-based lakehouse solutions using Databricks, Spark, Delta Lake, and modern ETL/ELT patterns. Establishes data quality, governance, lineage, observability, security, and compliance capabilities; modernizes legacy platforms; optimizes large-scale workloads; and drives engineering standards. Partners cross-functionally, influences architecture and roadmaps, mentors engineers, resolves complex dependencies, and supports technical hiring.

Leads the design and implementation of enterprise data platforms, ETL/ELT pipelines, lakehouse architectures, and distributed processing systems supporting credit risk and analytics. Establishes data quality, governance, lineage, security, monitoring, and operational standards while modernizing legacy assets in the cloud. Partners with product, risk, architecture, and data science teams, mentors engineers, influences technical strategy, optimizes performance and cost, supports compliance, and drives complex cross-functional initiatives.

Lead the design and modernization of enterprise data platforms and pipelines supporting credit risk, decisioning, regulatory reporting, analytics, and AI initiatives. Architect cloud-based ETL/ELT, streaming, and lakehouse solutions using Databricks, Spark, Delta Lake, and related technologies. Establish data quality, governance, lineage, security, observability, and operational standards. Provide technical leadership, mentor engineers, influence architecture and roadmaps, optimize workloads, and collaborate across product, risk, analytics, architecture, and business teams.

Leads the design and implementation of enterprise data platforms and pipelines supporting credit risk, decisioning, analytics, regulatory reporting, and AI initiatives. Architects cloud-based lakehouse solutions using Databricks, Spark, Delta Lake, and related technologies; establishes data quality, governance, lineage, observability, and security controls; modernizes legacy systems; optimizes large-scale processing; and mentors engineers while influencing cross-functional technical strategy.

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.

Data Engineers will support a large-scale migration from Oracle to Microsoft Fabric. Responsibilities include developing data pipelines, metadata-driven ingestion and orchestration frameworks, semantic model migration, data mirroring, integration testing, analytics data modelling, and Microsoft Purview scanning, cataloguing, and governance. The role involves collaborating across Agile workstreams to deliver scalable, high-quality data modernisation outcomes.

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 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 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 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 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 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 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.