Data Engineer

Landauer is a leader in radiation safety and dosimetry solutions, serving healthcare, research, and industrial customers across the United States.

We are investing in the next generation of our software platform — a cloud-native, AI-powered suite that automates compliance workflows, dose analysis, and program management for radiation safety professionals.

We are looking for a seasoned Data Engineer to take a lead role in shaping the data foundation across Landauer's growing portfolio of products.

You will set the standards for how data is modeled, moved, and governed — owning the full data lifecycle from ingestion through analytics and AI feature delivery.

This is a high-impact, high-autonomy role on a small, focused team where your architectural decisions will directly influence product outcomes across multiple initiatives.

You will serve as a key technical voice alongside backend engineers, product managers, and data scientists.

• Lead the design and implementation of scalable ETL/ELT pipelines that ingest dosimetry, dose report, and compliance data from Landauer and external systems, establishing patterns the team builds on
• Own the data storage architecture across structured, unstructured, and cached tiers on AWS — making deliberate trade-off decisions on cost, latency, and consistency
• Architect event-driven data flows for asynchronous processing of compliance events, threshold breaches, and regulatory alerts at scale
• Design and evolve data models that power dose reporting, equipment calibration tracking, regulatory audit exports, and statistical outlier detection — balancing OLTP performance with analytical query needs
• Drive query optimization and data access strategies for APIs serving real-time compliance dashboards, establishing benchmarks and SLOs
• Lead data infrastructure work for AI/ML features, including dataset curation, feature engineering, and pipeline design supporting cloud-native AI capabilities
• Define and enforce data quality standards, schema contracts, lineage tracking, and observability practices across all pipeline stages
• Own compliance posture for data handling under HIPAA, NRC, and applicable state regulations — including access control models, encryption at rest/in transit, and immutable audit logging
• Establish monitoring and alerting for pipeline health, data freshness, and SLA adherence; lead incident response and post-mortems for data issues
• Act as the technical authority on internal data contracts and API schemas in cross-functional discussions with backend, product, and AI/ML teams

• 5+ years of experience in data engineering, with a proven track record delivering production-grade pipelines, data models, and storage architectures
• Deep SQL expertise; hands-on experience designing schemas and tuning queries at production scale
• Extensive, hands-on experience with AWS cloud data services for storage, compute, messaging, and eventing
• Advanced Python proficiency for pipeline development; experience structuring code for maintainability and reuse
• Proven experience designing and operating event-driven architectures and queue-based or streaming ingestion systems
• Strong data modeling skills across both OLTP and OLAP workloads, with the ability to balance normalization, query performance, and downstream analytics needs
• Deep understanding of data security and compliance practices: field-level encryption, IAM least-privilege, PII classification, and audit trail design
• Experience setting team-level standards: data contracts, schema registries, lineage tracking, or data quality frameworks

• Hands-on experience building data infrastructure for LLM fine-tuning, RAG pipelines, or agentic AI systems on AWS
• Demonstrated experience operating in a serverless-first environment and making principled architectural decisions on cost, scalability, and performance
• AWS certifications (Data Engineer Associate, Solutions Architect Associate or Professional, or equivalent)

• Meaningful work with direct impact on patient and worker safety across thousands of licensed US facilities
• Greenfield data platform work on a modern AWS stack — you will shape the architecture, not inherit it
• Senior-level autonomy with cross-functional influence across product, backend engineering, and AI/ML teams
• Competitive senior compensation package, equity participation, comprehensive benefits, and a dedicated professional development budget