Top Data Engineer Jobs in Dublin

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 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.

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.

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 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 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.

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.

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.

Develop and maintain scalable data pipelines, integrations, transformations, data models, and quality controls using Snowflake, Airflow, Azure, SQL, and Python. Implement data masking, monitoring, automation, CI/CD, and ETL/ELT solutions while collaborating with architects, analysts, and data stewards. Support reliable data foundations for reporting, analytics, and machine learning, using Agile delivery practices and AI-assisted engineering tools.

Design and build scalable Databricks and Azure data platforms, ETL/ELT pipelines, Lakehouse architectures, and production-grade Generative AI solutions. Develop RAG pipelines, multi-agent workflows, MLOps processes, feature stores, and AI training datasets. Implement data governance, quality monitoring, event-driven integrations, and CI/CD practices using Azure services. Collaborate with cross-functional teams, establish engineering best practices, and mentor engineers in AI and data technologies.

Build a reusable Python ingestion framework to replace legacy Java ETL, develop real-time PostgreSQL-to-Snowflake streaming and CDC pipelines, and improve customer onboarding with LLM-assisted schema mapping. Own production deployment, monitoring, data quality, recovery, and secure handling of sensitive data. Collaborate with the Data and AI Platform team to design infrastructure and make pragmatic architectural decisions.

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.

Design, develop, and maintain scalable data pipelines and architectures for analytics and reporting. Perform ETL and streaming data integration, data modeling, governance, quality management, and workflow optimization across cloud and big data environments. Translate business requirements into data solutions, monitor reliability and performance, and collaborate with stakeholders to advance Walmart’s data strategy.

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.

Designs and delivers scalable batch and streaming data solutions on Azure and Microsoft Fabric. Builds lakehouse, warehouse, and semantic layers for analytics and AI; implements data quality, metadata, lineage, monitoring, security, and CI/CD. Owns technical workstreams from design through production, optimizes performance and cost, manages stakeholders, reviews designs, establishes engineering standards, and mentors data engineers.

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.