Top Tech Jobs & Startup Jobs in Dublin

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.

Manages AV operations across two Ireland offices, ensuring meeting rooms, collaboration spaces, events, and executive communications remain reliable. Oversees Microsoft Teams Rooms, conferencing systems, maintenance, upgrades, vendor relationships, incident escalation, documentation, and service improvements. Partners with stakeholders to gather requirements, deliver AV projects, maintain standards, support budgeting and lifecycle planning, and provide high-quality user experiences.

Leads end-to-end software engineering for scalable analytics applications, platforms, data workflows, and AI-powered solutions. Responsibilities include system design, development, testing, deployment, production support, Python/PySpark/SQL pipeline development, cloud application development, DevOps automation, technical leadership, prototyping, code reviews, and cross-functional collaboration.

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.

Leads end-to-end software engineering for analytics platforms and applications, including architecture, development, testing, deployment, support, and technical direction. Builds scalable AI-powered solutions and large-scale data pipelines with Python, PySpark, and SQL. Evaluates emerging AI tools, maintains GitHub Actions CI/CD, supports cloud development, conducts reviews, creates prototypes, documents systems, and partners with product, architecture, data science, and engineering teams.

Design, deploy, and operate production AI agents and generative AI solutions using Azure OpenAI, Snowflake, Databricks, retrieval, semantic models, and tool orchestration. Build reliable data workflows, governance, security, monitoring, evaluation, and responsible AI controls for sensitive healthcare data. Integrate AI systems with enterprise applications and APIs, troubleshoot performance and retrieval issues, and collaborate with technical and business stakeholders to deliver compliant, scalable solutions.

Build and productionize generative AI and classical ML solutions for healthcare: data preparation, feature engineering, model/prompt development, evaluation, deployment, MLOps, monitoring, retrieval/indexing, and responsible AI practices while collaborating with cross-functional stakeholders.