Staff Data Engineer

Intellias is developing a large-scale digital healthcare initiative that provides the data and AI foundations for personalized healthcare experiences. The Staff Data Engineer will design and build scalable FHIR-based data infrastructure, real-time and batch pipelines, backend services, and distributed systems while providing technical leadership, architectural direction, and mentorship across teams.

Responsibilities:

• Design and implement solutions for large-scale, ambiguous data engineering and distributed-systems challenges
• Architect, build, and evolve real-time and batch data infrastructure supporting operational, analytical, and AI/ML workloads
• Design and scale event-driven data pipelines using technologies such as Kafka, Spark/PySpark , and modern data-processing platforms
• Build backend services and APIs using Python, FastAPI , and cloud-native technologies to support product and platform capabilities
• Define scalable approaches to data ingestion, transformation, modeling, validation, storage, and delivery
• Establish and champion technical standards, architectural patterns, engineering best practices, and reusable platform components
• Serve as a cross-team technical authority for data systems, pipelines, distributed processing, and platform architecture
• Improve reliability, scalability, performance, and cost efficiency across data infrastructure
• Implement robust observability, logging, monitoring, and alerting across distributed data systems
• Design solutions with security, privacy, healthcare compliance, and scalability built in from the beginning
• Improve engineering workflows, CI/CD pipelines, automated testing, and production deployment practices
• Collaborate closely with Product, ML/AI, Platform, Infrastructure, and application engineering teams to build shared data capabilities
• Provide technical mentorship to engineers through architecture reviews, design discussions, pairing, and knowledge sharing
• Evaluate new data and AI technologies and introduce them where they provide measurable improvements to platform capabilities or engineering efficiency
• Remain hands-on with production engineering , validating architectural decisions through implementation and operational experience

Requirements:

• • 8+ years of professional software and data engineering experience , including at least 3 years working with large-scale distributed data systems
• • Deep expertise designing and building scalable data platforms, pipelines, and distributed processing systems
• • Strong hands-on proficiency in Python , including production experience with technologies and libraries such as PySpark, Pandas, and FastAPI
• • Strong experience with Apache Spark/PySpark and distributed data processing at scale
• • Experience designing real-time and batch data pipelines using technologies such as Kafka, Spark, or comparable distributed processing platforms
• • Strong understanding of data architecture, data modeling, schema evolution, data quality, and heterogeneous data integration
• • Experience with workflow orchestration technologies such as Prefect, Apache Airflow , or equivalent
• • Experience building production backend services and APIs supporting data and platform capabilities
• • Hands-on experience with SQL and modern data storage technologies, including relational and NoSQL systems such as MongoDB
• • Strong understanding of distributed systems principles , including scalability, reliability, fault tolerance, consistency, and performance
• • Experience designing and implementing observability, logging, monitoring, and alerting for distributed data systems
• • Strong cloud-native engineering experience, including Docker, Kubernetes, CI/CD , and production deployment practices
• • Proven ability to independently design solutions for ambiguous, large-scale engineering problems spanning multiple systems and teams
• • Demonstrated experience establishing technical standards, engineering patterns, development practices, and reusable platform capabilities
• • Strong technical leadership skills with experience mentoring senior engineers, conducting design reviews, and influencing architecture without relying on formal people-management authority
• • Strong understanding of security, privacy, and data protection principles for production data platforms
• • Professional English sufficient for direct collaboration with U.S.-based engineering, product, ML, and platform teams
• • Experience with FHIR, HL7, C-CDA , or other healthcare interoperability standards
• • Previous experience in healthcare, HealthTech, or another regulated data environment
• • Understanding of HIPAA, HITECH , or comparable data privacy and compliance requirements
• • Experience deploying or supporting LLMs, ML models, AI-enabled data products, or retrieval-based systems
• • Experience building data infrastructure supporting ML/AI workloads and production inference
• • Hands-on experience with observability technologies such as OpenTelemetry, Datadog, Prometheus , or similar platforms
• • Experience with DuckDB or modern analytical data-processing technologies
• • Experience with AI-assisted engineering tools such as Claude Code, GitHub Copilot, Cursor, or comparable solutions
• • Contributions to open-source projects or publicly available engineering initiatives