Top Data Engineer Jobs in Dublin

Design and implement scalable Databricks data platforms, Lakehouse architectures, ETL/ELT pipelines, Generative AI solutions, RAG pipelines, multi-agent workflows, and MLOps capabilities. The role includes data quality governance, Azure integrations, feature-store and training-dataset development, CI/CD practices, mentoring engineers, and contributing to enterprise AI engineering standards.

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.

Designs, implements, and maintains scalable data solutions and ETL pipelines using SQL Server, relational databases, Databricks, and Spark. Responsibilities include data modeling, SQL optimization, data validation, troubleshooting pipeline issues, and communicating technical concepts to diverse stakeholders. The role requires managing competing priorities, supporting multiple projects, innovating, and solving data problems with minimal supervision.

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.

Own EULER’s data lifecycle, including instrumentation, validation, reconciliation, privacy, anonymization, canonical data modeling, analytics datasets, documentation, and production pipelines. The role requires strong SQL, software engineering practices, independent ownership, skepticism toward data quality, and clear communication. Preferred experience includes GDPR/LGPD, multi-tenant B2B SaaS, dbt, statistics, Cloudflare Workers, Postgres, Parquet, DuckDB, and Bubble.

Build and maintain ETL/ELT pipelines, cloud data warehouse models, dashboards, and reports. Ensure data quality, governance, lineage, and reliable metric definitions while translating stakeholder needs into actionable analytics. Support AI-enabled reporting, natural-language querying, anomaly detection, and governed LLM tools. Collaborate with business, IT, and engineering teams to automate reporting and improve self-service data access.

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.

Design, deploy, and operate highly available database infrastructure across multi-cloud, virtualized, and Kubernetes environments. Migrate database services from virtual machines to Kubernetes, support disaster recovery and backup strategies, tune performance, and implement observability, logging, and alerting. Collaborate with engineering teams to resolve infrastructure challenges and automate workflows using scripting and infrastructure orchestration tools.

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.

Lead mechanical engineering design and technical delivery for complex data centre projects. Develop resilient cooling, ventilation, automation, control, and ancillary systems; coordinate with electrical and multidisciplinary teams; supervise engineers; manage resources, deadlines, and client relationships; and review detailed designs. The role also involves applying building physics, CFD, sustainable design principles, CAD, Bluebeam, and BIM-based collaboration.

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.

Develop and optimize Supabase’s Data API based on PostgREST. Responsibilities include improving the PostgreSQL driver and performance, developing features, fixing bugs, resolving incidents and escalated support issues, improving observability, and debugging integrations across the Supabase platform. The role requires strong Haskell development, PostgreSQL and SQL expertise, Linux debugging, open-source contribution, and technical user support.

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.

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, 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.