Role Description We are looking for a strategic and hands-on Data Domain Engineering Director to lead domain-based data product teams within Global Digital Engineering. In this role, you will be responsible for the technical delivery, operational reliability, and continuous evolution of high-value data products built on a unified data platform. You will work as a peer and strategic partner to business data domain leads, translating business priorities into scalable, reliable, and reusable data products. - Own the end-to-end lifecycle of domain data products, including design, development, operationalization, evolution, and retirement. - Lead technical delivery across data ingestion, pipelines, transformations, APIs, and data engineering processes within the domain. - Build data products using the unified data platform and ensure consistency and quality through a standardized data product journey. - Define and enforce domain-level data product standards in line with enterprise governance, interoperability, and quality requirements. - Ensure discoverability, usability, documentation, metadata quality, and adoption of domain data products. - Take accountability for operational reliability and stability, including service level objectives, monitoring, and continuous improvement. - Partner closely with business domain owners as a technical co-leader, ensuring equal standing and direct collaboration on priorities and outcomes. - Coordinate with other data domain engineering directors, the data platform leadership, and data foundation teams to enable consistent and seamless delivery. - Communicate technical decisions, risks, dependencies, and progress clearly to senior stakeholders and management. - Establish and continuously improve release, operations, quality, and engineering processes. - Develop and lead high-performing data engineering and data product teams through hiring, coaching, staffing, and capability development. - Manage domain-level technical debt and architectural evolution, balancing delivery speed with long-term maintainability and scalability. Qualifications - Strong expertise in data product architecture and domain-oriented data product design. - Proven experience building and operating scalable data products on modern cloud-based data platforms. - Deep understanding of data engineering practices, including data ingestion, pipelines, transformations, and API-based data access. - Ability to ensure operational stability, reliability, and quality of data products through service level objectives, monitoring, and observability. - Strong understanding of data product management, including product value, consumers, lifecycle, adoption, and service levels. - Ability to translate business priorities into domain data products aligned with measurable business outcomes. - Experience working in federated data operating models and domain-based data ownership structures. - Ability to drive interoperability, standardization, and reuse of data products across domains. - Experience managing cross-domain dependencies and prioritization in a Data Mesh environment. - Proven leadership experience managing data engineering and data product teams. - Experience staffing, developing, and coaching teams with the right mix of engineering, product, and domain capabilities. - Ability to operate as a technical co-leader and strategic partner to business domain owners. - Strong stakeholder management skills and the ability to align business priorities with technical delivery. - Experience driving cross-domain collaboration in federated data organizations. - Ability to build a strong data product mindset and ownership culture focused on accountability, quality, and continuous improvement. Additional Information - Please be informed that our remote working possibility is only available within Hungary due to European taxation regulation.

• Support and maintain the mission-critical Cost Roll process by ensuring accurate aggregation of healthcare claims data and precise matching of claims to provider demographic data. • Maintain and monitor data workflows that aggregate healthcare claims data for the Cost Roll process. • Validate and reconcile claims and provider demographic data to ensure accurate provider matching and data quality. • Investigate, troubleshoot, and resolve data issues that could impact financial, clinical, and provider management systems. • Perform data validation, quality checks, and operational support activities to ensure clean and reliable datasets. • Collaborate with stakeholders to support ongoing data processing, reporting, and operational reliability initiatives.

**What Your Day Might Look Like:** - Design, build, and maintain data pipelines that serve models, data, and AI workflows to internal and client-facing applications. - Work across database types — relational, vector, and graph — modelling and storing data appropriately for each access pattern, in close collaboration with data scientists. - Build and maintain dbt models — writing transformation logic, tests, and documentation that ensure data quality and traceability end-to-end. - Operate what you build: instrument pipelines with logging, metrics, and tracing; diagnose and resolve production data issues before they become someone else's problem. - Write clean, tested, production-quality code and contribute to CI/CD pipelines and infrastructure-as-code. - Show up to code reviews, design discussions, and retrospectives — and have something worth saying.

• Architect and build production data pipelines and data platforms that serve models, data, and AI workflows to internal and client-facing applications — and stay accountable for them under live conditions. • Own non-functional quality across your domain: latency and throughput budgets, scalability, reliability, observability, and cost. • Lead the design and operation of multi-model data stores — relational (PostgreSQL, MySQL), vector (Pinecone, Weaviate, pgvector), and graph (Neo4j, Neptune) — applying the right tool to each access pattern, not the most fashionable one. • Set technical direction: write design docs, make build-vs-buy calls, and defend your approach with evidence rather than instinct. • Work across the stack when the problem demands it — services, data access, infrastructure-as-code, CI/CD — and diagnose it when things drift in production. • Raise the floor for the team: mentor mid-level and junior engineers, run rigorous code reviews, and hold the quality bar without making it someone else's job to ask you.