Top Data & Analytics Jobs in Dublin

Leads the analytics strategy, roadmap, growth agenda, and portfolio expansion for Retrospective Coordination of Benefits and Reverification. Partners with Product, Operations, Technology, Growth, and Analytics teams to identify use cases, build business cases, measure financial and operational value, and drive adoption of predictive analytics, GenAI, and Agentic AI solutions. Advises senior leadership and resolves complex business challenges through enterprise-scale analytics initiatives.

Senior leader responsible for designing, delivering, and improving BI, reporting, analytics, and data management. Partners with stakeholders to prioritize initiatives, ensure data quality, define metrics, drive automation and AI-first improvements, and lead a team of analytics specialists to support operational and strategic decision-making.

Develop and maintain scalable data pipelines integrating databases, APIs, logs, streaming platforms, and external providers. Build transformation and processing jobs, implement governance and de-identification controls, monitor pipeline reliability, and establish automation and deployment best practices. Partner with analytics, product, infrastructure, and data science teams to advance cloud-based data and analytic platforms using Azure, Databricks, Snowflake, Spark, and SQL.

Leads a data engineering team responsible for integrating complex data sources, developing pipelines, designing enterprise data architectures, and migrating on-premises platforms to the cloud. Manages stakeholders, mentors engineers, oversees project implementations, establishes engineering standards, and partners with data science and infrastructure teams. The role requires expertise in big data, cloud databases, streaming technologies, data modeling, data quality, and healthcare data integration.

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.

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.

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.

Provides strategic and operational leadership for a Provider Growth analytics portfolio. Defines analytics strategy, leads multidisciplinary teams, owns analytics products, and drives AI and machine learning adoption. Develops solutions for provider targeting, segmentation, market prioritization, campaign optimization, and growth opportunities. Partners with executives and technical teams, manages roadmaps, budgets, governance, delivery quality, talent development, and enterprise-scale data and AI capabilities.

Build and maintain scalable ETL pipelines, integrate data from databases, APIs, streaming platforms, and external providers, and transform large datasets for analytics and machine learning. Establish reusable engineering frameworks, data governance, monitoring, alerting, and reliability practices. Collaborate with analytics and business teams on cloud data migration, predictive model enablement, and healthcare cost reduction. Required skills include SQL, Python, Scala or Java, cloud platforms, Snowflake or Databricks, DevOps, Git, and CI/CD.