Top Data Engineer Jobs in Dublin

Design, build, and maintain ETL/ELT data pipelines and processing jobs (PySpark/SQL) on cloud platforms (Azure, Databricks, Snowflake). Ensure data quality, governance, monitoring, and automation; partner with analytics and infrastructure teams to productionalize analytic pipelines and support platform architecture.

Design, develop, and maintain ELT/ETL Snowflake data warehouse solutions; implement data standards, CI/CD, monitoring, and production support; collaborate with product, business, and vendors; mentor junior developers and improve data engineering 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.

Design, build, and operate scalable Databricks data pipelines and Lakehouse data models using SQL, Python, and PySpark. Implement ELT/ETL and streaming patterns, data quality checks, Unity Catalog governance, performance optimization, monitoring, documentation, and operational support.

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

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 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.

Hands-on senior data engineer to design and build scalable Lakehouse data solutions, develop ETL/ELT and streaming pipelines, tune Spark workloads, manage Delta Lake ingestion and governance, implement infrastructure with Terraform, enforce IAM and data security, monitor pipeline health, collaborate in Agile with data scientists and stakeholders, and explore AI tooling to enhance the platform.

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.

The role involves developing and maintaining data models, managing data pipelines, ensuring data quality, and collaborating with business teams to create robust data solutions.

The Senior Data Engineer designs and builds data models and pipelines, ensures data quality and lifecycle management, and collaborates with cross-functional teams.

Operate and support Kafka clusters (MSK and on-prem), manage Kafka Connect ecosystem (Debezium, KSQL, Schema Registry), troubleshoot producers/consumers, implement HA/backup strategies, automate provisioning (IaC/scripting), monitor and tune cluster performance, support incident response and on-call, and document operational procedures.

Design, build, and operate enterprise reporting and analytics platforms. Create scalable ELT/ETL pipelines, master data foundations, and lakehouse/warehouse semantic models. Deliver Power BI datasets and dashboards, implement data quality, governance, DevOps practices, and cost-management data solutions. Partner with finance and stakeholders and provide technical leadership across architecture, deployment, and run-state support.

Lead technical direction for ResMed's data ecosystem: design Snowflake/dbt models, build resilient data pipelines (Python, Spark, APIs), establish governance and CI/CD (GitHub Actions), mentor engineers, and enable classical and generative AI/ML feature pipelines and workflows.

As a Senior Data Engineer, you'll build and manage data pipelines, ensure data quality, and collaborate across teams, contributing to the Data Platform's growth.

As a Data Engineer, you will develop and manage data pipelines, ensuring reliable data ingestion and delivery while optimizing for simplicity and performance.

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.

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.

Lead migration from Snowflake to the client platform, build AI-native data migration capabilities, design and maintain scalable data pipelines and platforms, propose improvements, and collaborate with senior engineers, product/project managers, and data analysts.

Design, build, and operate high-frequency, event-driven data pipelines into Snowflake; implement reverse ETL; model data with dbt; deploy cloud infra as code; monitor and improve production reliability, latency, quality, and cost; mentor teammates and establish data engineering standards.

Own and scale the ingestion pipeline and backend abstractions for high-throughput AI tracing data. Optimize ClickHouse schemas and queries, operate Langfuse Cloud and self-hosted deployments, and build infrastructure-heavy features that enable product engineers to ship data-heavy capabilities.

Design, build, and maintain large-scale batch ETL pipelines and data lakehouse systems. Partner with Data Science, Product, and Engineering to define data ontology, develop tooling and automation, establish data operations standards and SLAs, mentor junior engineers, optimize large datasets, triage infrastructure issues, and prototype new metrics and analytics solutions to support AI-driven capabilities.

Design, build, and own end-to-end data pipelines on BigQuery and dbt; orchestrate workflows with Dagster; enable analytics and AI use cases; build marketing attribution and reverse-ETL; enforce data quality, observability, and platform standards.

Build and scale a blockchain data platform handling petabytes of on-chain data. Design and implement distributed systems, data ingestion/decoding pipelines, and performant SQL processing. Write production code (Kotlin, Go, Java, Rust), improve developer experience, automate operations (including AI-driven automation), and own platform components.

The Lead Data Analytics & AI Engineer will lead the technical strategy and engineering best practices for data modeling, ingestion, and advanced analytics, while mentoring engineers and collaborating across teams.