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.

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 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.

Designs and maintains scalable Azure data pipelines, warehouses, models, ETL/ELT processes, dashboards, and analytics solutions. Uses Azure Data Factory, Azure Synapse, Power BI, Python, and SQL to transform data, optimize performance, resolve quality issues, and support business decisions. The role also involves Azure DevOps, CI/CD, data governance, GDPR compliance, Agile delivery, stakeholder collaboration, and leading data engineering initiatives.

Develop and maintain scalable data pipelines integrating databases, APIs, logs, streaming platforms, and external providers. Build transformation and processing jobs, implement governance and de-identification controls, monitor pipeline reliability, and establish automation and deployment best practices. Partner with analytics, product, infrastructure, and data science teams to advance cloud-based data and analytic platforms using Azure, Databricks, Snowflake, Spark, and SQL.

Build and maintain scalable production data pipelines, machine learning data pipelines, and data transformation frameworks for financial crime detection solutions. Collaborate with product, R&D, data, analytics, customers, and partners; optimize large-scale data architectures; support feature engineering and root cause analysis; and train customer teams. The role requires customer-facing collaboration, English and Spanish fluency, and domestic and international travel when needed.

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

Develops and governs clinical data standards and study specifications aligned with CDISC and regulatory guidance. Partners with statisticians and programmers to create compliance and productivity tools, mentors teams, monitors standards and regulatory updates, supports submissions, and represents the organization in industry standards initiatives. Requires extensive programming, clinical trial data, regulatory submission, mentoring, and CDISC expertise.

Build and maintain data pipelines and infrastructure for analytics, experimentation, and company metrics. Collaborate with machine learning engineers on training, quality assessment, and model release automation. Improve data delivery processes, automate manual workflows, and develop scalable data models. The role requires strong Apache Spark and Python expertise, SQL proficiency, Git experience, and familiarity with cloud data platforms and Kubernetes.

Build and maintain data pipelines, infrastructure, data models, and automation for analytics and machine learning workflows. Collaborate with ML engineers on model training, quality assessment, and releases. Improve internal data delivery processes and enable efficient access to large-scale organizational data.

Designs, builds, and maintains scalable Databricks data pipelines, ETL/ELT workflows, cloud data platforms, data lakes, and data warehouses. Implements data quality, governance, lineage, and monitoring frameworks using tools such as Atlan and Monte Carlo. Partners with stakeholders, BI teams, and engineers to develop data models and analytics-ready datasets while optimizing performance, scalability, security, and cost efficiency.

Build and operate AWS-based data platforms for fleet telematics products, including streaming and batch pipelines, ETL, data quality validation, observability, retention enforcement, and customer reporting. Own data engineering workstreams from architecture and design through deployment and production monitoring. Optimize database performance, cloud costs, and large-scale data systems while investigating production data issues and ensuring compliance.

Lead mechanical engineering design and technical delivery for complex data centre projects. Design resilient cooling, ventilation, automation and control systems; coordinate with electrical and multidisciplinary teams; manage project timelines and resources; supervise engineers; review technical work; produce detailed designs using CAD and BIM tools; and build strong client relationships. The role also involves applying building physics, CFD, sustainability principles, and critical-systems expertise.

Designs, develops, tests, optimizes, and maintains scalable batch and near-real-time data pipelines and architectures. Ensures data quality, builds API integrations, improves internal data processes, and supports machine learning, data science, BI, and product initiatives. The role requires experience with data warehouses, relational and NoSQL databases, data modeling, event-driven architectures, SQL, Python, Airflow, Spark, and AWS data services.

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.

Own the data engineering side of DoiT’s integrations framework by building and maintaining third-party billing and usage API integrations. Ensure data correctness, completeness, normalization, reconciliation, and support for negotiated rates. Manage orchestration, scheduling, backfills, and pipeline operations while using AI throughout development. Collaborate with product, support, and engineering teams to expand vendor coverage and improve cloud and SaaS spend visibility.

As a Data Engineer at Yahoo, you'll implement AI/ML capabilities, build data pipelines, optimize workflows, and collaborate with teams to enhance user experiences.

Owns the engineering and evolution of digital behavioral telemetry, web telemetry, RUM, and conversion data. Designs event taxonomies, schemas, contracts, data models, and governed data products supporting analytics and conversion measurement. Partners with product and engineering teams on instrumentation, observability, data quality, privacy, testing, and lifecycle controls. Serves as the senior technical subject matter expert for telemetry and behavioral data while enabling analysts and stakeholders to use trusted data products.

Own data engineering for a cloud and SaaS billing integrations platform. Build and maintain third-party billing API integrations, operate ingestion pipelines, normalize vendor data, manage orchestration and backfills, and ensure financial-grade correctness through reconciliation and quality checks. Use AI throughout the engineering workflow and collaborate with product, support, and platform teams to expand vendor coverage and improve customer cost visibility.

Own data engineering for a cloud and SaaS billing integrations framework. Build and maintain third-party billing and usage API integrations, ensure data correctness and reconciliation, normalize vendor data, and manage orchestration, scheduling, ingestion, and backfills. Use AI throughout the engineering workflow, collaborate with product and support teams, and improve the scalability and reliability of pipelines that support financial decision-making.

Develop and optimize large language models and NLP capabilities for decision support, mission planning, workflow automation, and situational awareness. Responsibilities include data preprocessing, model training and fine-tuning, RAG and agentic system development, evaluation, deployment, monitoring, documentation, and collaboration with engineering and product teams. The role requires strong Python, deep learning, LLMOps, cloud, and MLOps expertise, plus knowledge of responsible AI and bias mitigation.

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.

Build and maintain scalable GCP data pipelines, BigQuery data models, enterprise semantic layers, and CI/CD workflows for a modernized marketing data platform. Rebuild legacy pipelines, support DataHub adoption, enforce data quality, optimize performance and costs, and enable secure multi-brand marketing reporting. Collaborate with product, Martech, analytics, and data engineering teams.