Data engineer in healthcare

Intellias is looking for a Senior Data Engineer to join a digital healthcare initiative focused on building a large-scale health data platform that helps millions of people discover healthcare providers, connect with their health information, and make better-informed care decisions. This is a hands-on, high-ownership engineering role for someone experienced in building and operating data-intensive systems at scale. You will own critical data ingestion and processing capabilities, improve data quality and reliability, and develop AI-powered solutions for complex real-world healthcare data challenges.

• Design, build, and operate scalable data ingestion pipelines that onboard healthcare provider data from EHR systems, national registries, healthcare networks, and partner feeds.
• Transform heterogeneous source data into standardized, trusted data models, including FHIR-native resources.
• Own data quality end-to-end, including validation rules, quality thresholds, confidence scoring, automated monitoring, and alerting.
• Design and implement solutions for entity resolution, deduplication, relationship inference, classification, enrichment, and data quality scoring.
• Build and evolve distributed data processing pipelines using Python, Spark, and modern orchestration technologies.
• Improve pipeline reliability, scalability, observability, and performance so that new data sources can be onboarded efficiently and safely.
• Establish and evolve data governance standards, including schemas, staging models, validation processes, and data refinement workflows.
• Design and operate data storage and search solutions supporting large-scale healthcare datasets.
• Build AI-powered data tooling that improves the accuracy, automation, and intelligence of data processing workflows.
• Use modern AI development tools as an integral part of the engineering workflow to accelerate implementation, testing, debugging, documentation, and data analysis.
• Own production operations for the solutions you build, including monitoring, troubleshooting, incident response, and root-cause analysis.
• Partner with Analytics and Data Science teams to develop data quality dashboards, metrics, and reporting.
• Collaborate with Product and Business stakeholders to prioritize data sources and improvements based on customer and business impact.
• Contribute to technical design reviews and help establish scalable engineering patterns across the broader data platform.

• 5+ years of professional experience building and operating data-intensive backend or data engineering systems at scale.
• Strong hands-on expertise in Python, including development of production-grade data processing and backend services.
• Strong experience designing and building scalable data ingestion and ETL/ELT pipelines for large and heterogeneous datasets.
• Sound data engineering instincts, including data modeling, storage design, schema evolution, query optimization, caching strategies, and selecting appropriate batch or streaming processing patterns.
• Hands-on experience with Apache Spark or Databricks for distributed data processing.
• Experience with workflow orchestration technologies such as Prefect, Apache Airflow, or equivalent.
• Strong understanding of data modeling, schema design, validation strategies, data transformation, and data quality management.
• Experience designing solutions for data deduplication, entity resolution, data enrichment, classification, or record linkage.
• Experience with both relational and document-oriented databases; practical knowledge of technologies such as MongoDB or comparable NoSQL platforms.
• Experience with search technologies such as OpenSearch, Elasticsearch, or comparable solutions.
• Strong AWS experience, including services such as S3, ECS/EKS, Lambda, or equivalent cloud-native technologies.
• Hands-on experience with Docker, Kubernetes, CI/CD, and operating cloud-native workloads in production.
• Strong understanding of production data pipeline concerns including reliability, fault tolerance, observability, monitoring, alerting, and incident response.
• Experience establishing and maintaining data governance standards, schemas, validation rules, and repeatable data refinement processes.
• Practical experience using AI-assisted software engineering tools as part of the regular development lifecycle.
• Strong ownership mindset and ability to independently identify data or engineering problems, propose solutions, implement them, and operate them in production.
• Strong communication skills and ability to collaborate directly with engineering, analytics, product, data science, and business stakeholders.
• Professional English sufficient for direct collaboration with U.S.-based teams.
• Будет плюсом: Experience working with healthcare data, HealthTech platforms, or healthcare interoperability; Knowledge of FHIR, particularly resources such as Practitioner, Organization, PractitionerRole, Endpoint, and Location; Experience integrating data from EHR/EMR systems, healthcare registries, provider networks, or similar complex data ecosystems; Experience designing data platforms handling PHI/PII or other regulated and sensitive information; Familiarity with HIPAA-related data protection and security considerations; Experience building AI-powered data processing solutions, particularly entity resolution, relationship inference, classification, enrichment, anomaly detection, or data quality scoring; Practical experience integrating LLMs into production data workflows, including evaluation, reliability, latency, cost, and data-protection considerations; Experience with AI engineering tools such as Claude Code, GitHub Copilot, Cursor, or similar platforms

• By creating a comfortable atmosphere in our team, we empower individuals to unlock their true potential and achieve extraordinary results.
• That’s why we offer a range of benefits that support your well-being and charge your professional growth.