Top Data Engineer Jobs in Dublin

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.

Lead global strategy and operations for a cloud-native Service Delivery Platform, ensuring secure, scalable, and reliable service delivery across multiple cloud environments.

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.

Build and evolve Toast’s cloud-native data platform, working hands-on with Spark, Iceberg, streaming infrastructure, APIs, and feature-store capabilities. Drive technical strategy and architectural decisions for distributed systems at scale, influence adjacent engineering and product teams, mentor engineers, and improve platform adoption and self-service across the organization.

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.

Designs, builds, and operates production-grade agentic AI systems and orchestration frameworks. Responsibilities include prompt architecture, tool and API integrations, monitoring, evaluation, cost controls, reliability improvements, and governance documentation. The engineer collaborates with data science, data engineering, and governance teams to ensure reliable, compliant workflows using structured healthcare and pharmaceutical data.

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.

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.

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

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.

Own end-to-end customer data migrations into the Vew platform, including source analysis, mapping, transformation, validation, reconciliation, dry runs, cutovers, and post-migration support. Develop and improve C#/.NET tooling, write complex SQL, resolve data quality and performance issues, and establish repeatable, auditable migration processes. Serve as the technical migration contact for customers while collaborating with architects, product, and engineering teams.

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.

Designs and builds scalable batch and streaming data platforms supporting AI products. Responsibilities include ingestion, processing, storage, indexing, retrieval, orchestration, data quality, observability, lineage, schema management, and reliability. Partners with ML and product engineering teams to productionize AI and retrieval-based features, contributes hands-on code, establishes engineering standards, and mentors engineers.

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.

Design and build durable, distributed data-streaming infrastructure that decouples process data from Camunda’s live execution engine. Develop customer-facing integrations such as Kafka connectivity, support analytics data consumption at scale, and make architecture decisions around consistency, durability, and storage. Participate in production support and on-call rotations while collaborating with Product, Data Layer, Distributed Systems, QA, SRE, and Support teams. Lead ambiguous, cross-functional engineering initiatives from design through delivery.