Top Data Engineer Jobs in Dublin

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.

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.

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 architecture and implementation of a scalable data lakehouse and streaming platform. Define technical strategy, build resilient systems, optimize performance, mentor engineers, and partner across teams to enable analytics and ML use cases using modern data platform technologies.

Build and maintain BigQuery data models and ETL pipelines using Dataform and Python; implement data quality and observability; schedule workflows with Cloud Composer/Airflow; support Looker reporting and ML feature pipelines; collaborate with Data Science and Product to productionize analytics.

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.

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.

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.

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.

Design, build, and maintain large-scale data pipelines, lakehouse and analytics systems. Develop frameworks, SLAs, complex queries, and collaborate with engineers and data scientists to deliver data-driven solutions and prototypes.

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.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

Design, build, and maintain ELT pipelines and dbt SQL models for analytics; optimize performance, scalability, and costs; apply AI-enabled tools to improve engineering workflows; collaborate to translate requirements into production-grade data assets.

Lead design and build of enterprise-scale cybersecurity data lakehouse ingestion, enrichment, and semantic layers. Scale pipelines from Splunk to cloud-native lakehouse, implement data observability and reliability, enable AI/advanced analytics, and mentor engineers to raise engineering maturity and modernize practices.

Own end-to-end AI-automated data platform migration projects (1-4 concurrently): scope, plan, execute, and hand off. Act as primary customer contact, configure Datafold's Migration Agent, partner with engineering on execution, and help refine delivery playbooks.

The Principal Data Engineer will improve data pipeline architecture, optimize data flows, and support data projects for various teams in the company.

Design, build, and maintain scalable batch and streaming data pipelines, internal tools, and APIs to support analytics, product features, and ML. Collaborate with product and engineering to improve data infrastructure, ensure reliability, testing, and observability in a cloud-native environment.

Design, build, and maintain scalable ELT data pipelines with Airflow; develop dbt data models and tests; optimize Athena tables/queries and data workflows; provision infrastructure with Terraform; ensure data quality and reliability; collaborate with Analytics, BI, and Engineering; troubleshoot pipeline and data quality issues.

Lead the design, development, deployment, and monitoring of scalable data pipelines, dashboards, and cloud-based architectures. Build systems that combine machine-learning insights from multiple data sources to improve product information. Collaborate with data, software, applied science, and product teams; maintain resilient microservices and data platforms; ensure high-quality, testable systems through reviews, automation, documentation, and mentorship.

The Software Engineer will manage data collection for model training, enhance the cloud infrastructure, and collaborate with the AI team to improve dataset quality and accessibility.

Build and operate GTM data systems covering account identity resolution, deduplication, corporate hierarchies, account and contact sourcing, enrichment, and Salesforce synchronization. Develop LLM-powered web research and enrichment pipelines, model data in dbt on Snowflake, orchestrate workflows with Airflow, and maintain quality controls. Partner closely with Sales, Marketing, RevOps, Partnerships, and Finance to create reliable data foundations that improve targeting, pipeline management, and revenue growth.

Design, build, and operate large-scale data pipelines and a Snowflake-based warehouse/lakehouse using Airflow, DBT, and Python. Integrate diverse data sources, enforce data quality SLAs, participate in on-call rotations, drive architecture and roadmap decisions, mentor engineers, and collaborate with ML/product teams to enable AI-ready data products.

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

Build and maintain AI-enabled business applications and integrations, help design and own application data models and databases, support AI architecture, data governance, ingestion and deployment, and collaborate across teams to translate business needs into scalable technical solutions.