Data Team Lead

Data Engineering Lead Our client is an established international (re)insurance organisation investing in a new technology and innovation team in Dublin. We are supporting an exciting greenfield build-out and are looking for a Data Engineering Lead to help shape a modern, cloud-native data platform from the ground up. About the Company Our client operates across global markets, providing specialist risk solutions to businesses across a range of industries. The organisation combines technical expertise, advanced analytics and sophisticated risk modelling to support complex underwriting and business decisions. With a strong focus on innovation and long-term growth, the company is investing in modern data infrastructure to enhance its analytical capabilities and support the development of new technology-driven solutions. About the Position This is a rare opportunity to join a genuine greenfield environment, building a modern data platform without the constraints of extensive legacy systems or complex migrations. As Data Engineering Lead, you will report to the senior leadership team responsible for risk analytics and data science. You will play a central role in designing and implementing the organisation’s data platform, working within a small, multidisciplinary team of actuaries, data scientists, AI engineers and platform engineers. The platform will be built on AWS and will integrate a broad range of internal and external datasets to support risk analytics, complex modelling workflows and proprietary business applications. This is a senior individual contributor role, ideal for an experienced data engineer who enjoys combining hands-on development with architectural decision-making. As the first dedicated engineer on the platform, you will help establish the architecture, tooling and engineering standards that underpin its future development. Key Responsibilities Data Platform Ownership & Architecture - Play a central role in the design and ongoing evolution of the data platform. - Lead the implementation of core platform components, including pipeline frameworks, data models and shared libraries. - Establish engineering standards focused on data quality, reliability, scalability and maintainability. - Design schemas and storage structures across different layers of the platform. Data Engineering & Delivery - Translate architectural designs and business requirements into robust, scalable implementations. - Design and build ingestion and transformation pipelines integrating fragmented datasets, modelling outputs, internal analytics and unstructured information. - Develop reusable libraries and components to enable consistent data access across applications. - Build and maintain datasets that support advanced analytics, AI and modelling workflows. Data Governance, Quality & Reliability - Define core data definitions, calculation logic and standards. - Design and implement a data catalogue and metadata framework. - Establish data validation, testing, monitoring and quality assurance practices. - Ensure pipelines provide appropriate levels of auditability, consistency, traceability and reproducibility. Collaboration & Engineering Excellence - Work closely with risk, underwriting, finance and operations stakeholders to ensure datasets are reliable, consistent and well-defined. - Collaborate with platform and integration teams to establish best practices across CI/CD, infrastructure-as-code and automated testing. - Provide technical guidance and mentorship to engineers and analysts working with data. Experience & Requirements Experience & Attributes - Experience in a Lead, Staff or Senior Data Engineering role with responsibility for technical direction and delivery. - Proven experience building and operating production-grade data pipelines in AWS. - Strong understanding of the end-to-end data lifecycle, including ingestion, transformation, storage and consumption. - Experience contributing to or designing data platform architecture. - A degree in Computer Science, Engineering, Mathematics, Physics or a related discipline, or equivalent practical experience. - A hands-on approach, strong problem-solving skills and the ability to work effectively in a collaborative, evolving environment. Technical Skills Programming & Data Processing - Strong Python and SQL skills. - Experience with data processing libraries such as Pandas, Polars, Dask or PyArrow. Cloud & Data Platforms - Practical AWS experience, ideally including S3, Glue, Lambda, ECS and DynamoDB. - Experience with Redshift or comparable analytical database technologies. Data Engineering - Strong ETL/ELT pipeline design and optimisation skills. - Data modelling and schema design. - Integration with APIs and external data providers. - Data quality, validation and monitoring. Software Engineering & DevOps - Git and GitHub. - CI/CD pipeline development. - Infrastructure-as-code. - Strong understanding of modern software engineering and cloud architecture principles. Nice to Have - Experience with open table formats such as Apache Iceberg, Delta Lake or similar technologies. What You’ll Build - A modern, cloud-native data platform on AWS. - Scalable data ingestion and transformation pipelines integrating internal systems, third-party models, APIs and unstructured data. - Canonical data models and consistent definitions supporting analytics and AI workflows. - Reusable data access libraries and shared platform components. - A data catalogue and metadata framework supporting dataset discovery and lineage. - Reliable, well-tested pipelines with strong monitoring, validation and traceability. The Opportunity This is an opportunity to take genuine technical ownership of a new data platform, influence architectural decisions from day one and work closely with data science, AI and business teams in a growing international organisation. If you enjoy building robust data infrastructure from first principles and want to see your engineering decisions shape a platform’s long-term direction, we would be keen to hear from you.