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.

Lead technical strategy and architecture for Partner Connect: design event infrastructure, APIs, partner onboarding and data storage/modeling; set frameworks and standards other teams use; mentor engineers; collaborate across product and platform to deliver scalable, observable systems; and adopt AI tools responsibly to improve developer experience and platform productivity.

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.

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 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.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

Develop backend and full-stack observability tools for MongoDB Atlas, including metrics collection, slow-query monitoring, and database performance recommendations. Design scalable web applications, troubleshoot production issues, lead medium-sized technical projects, collaborate with customers and support engineers, participate in on-call rotations, and improve engineering practices and operational systems.

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.

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.

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.

Designs, builds, operates, and improves scalable batch and near-real-time data pipelines, ETL/ELT workflows, data models, integrations, and enterprise data products. Owns solution quality, testing, governance, monitoring, security, deployment, and incident resolution. Partners with technical and business teams, leads designs, mentors engineers, and enables trusted data for analytics, automation, planning, and AI use cases.

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.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

Designs, builds, and maintains scalable data and machine learning infrastructure, pipelines, databases, integrations, self-service tools, and MLOps workflows. The role includes platform expansion, observability, security, data quality, performance optimization, infrastructure as code, CI/CD, documentation, and collaboration with data scientists, analysts, and stakeholders. Strong experience with cloud platforms, Kubernetes, containerization, databases, big data, orchestration, and AI-assisted development is required.

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.

Staff Software Engineer responsible for designing, building, and operating distributed market data systems. The role focuses on real-time blockchain data ingestion, streaming, normalization, storage, APIs, reliability, data correctness, observability, incident recovery, and production support. The engineer will lead technical decisions, mentor teammates, translate ambiguous requirements into designs, and use AI-assisted development throughout the software lifecycle.

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.

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.

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.

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.