Note: The job is a remote job and is open to candidates in USA. 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
Skills
- • 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
Company Overview