Industry Data Research & Validation

About the Role 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. Responsibilities

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