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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - **Company:** Tempus Inc - **Location:** Chicago, IL, United States - **Experience:** Expert - **Salary:** $125,000.0 - $180,000.0 - **Contract:** Permanent contract - **Skills:** Cerner, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Data Analysis, Application Frameworks, BigQuery, Health Informatics, Clinical Data Repository, Cloud Computing, Cloud Database, Cloud Storage, Information Engineering, Data Integrity, Data Profiling, Data Structures, Data Vault Modeling, Software Debugging, Dicom, Distributed Systems, Graph Database, Identity and Access Management, Interoperability, Python (Programming Language), PostgreSQL, Node.Js, NoSQL, Object-Relational Mapping, Operational Data Store, Operational Databases, Redis, Standard Sql, Software Engineering, Software Systems, SQL Databases, TypeScript, Unstructured Data, Google Cloud, Fast Healthcare Interoperability Resources, System Availability, Large Language Models, Multi-Cloud, Data Layers, Event Driven Architecture, Containerization, Kubernetes, Information Technology, Avro, Health Level Seven International, Data Management, NestJS, Terraform, Docker - **Published:** September 3, 2026 - **Apply:** https://www.dice.com/job-detail/8626e257-a9c3-484a-93c0-cb3ff0e3dbb3 ## About the Role * Data engineering depth. Proven track record building and operating production data pipelines that handle structured and unstructured data at scale, with real ownership of reliability and correctness. * Google Cloud fluency. Hands-on experience designing and running workloads on Google Cloud Platform - BigQuery, Pub/Sub, Cloud Storage, Cloud SQL, and Secret Manager - including the IAM and service-account model that controls access to sensitive data. * Analytics engineering. Strong SQL and practical experience with dbt or an equivalent transformation framework, including testing, documentation, and managing a model layer as it grows. * Infrastructure practice. Comfort owning infrastructure as code in Terraform, working in containers, and taking responsibility for the operational characteristics of what you deploy. * Software engineering ability. You write production-quality application and service code, not just pipeline glue - including APIs, tests, and the design work that goes with them. * Python and TypeScript. Python strong enough for production pipelines as well as hands-on data profiling and debugging, plus enough TypeScript or another statically typed language to work confidently in our service and application code. * Event-driven systems. Experience with pub/sub or queue-based architectures and the failure modes that come with them - retries, ordering, idempotency, and dead-letter handling. * Interoperability standards. Working knowledge of HL7, FHIR, and Epic/Cerner data structures, along with DICOM and genomic data formats. * Regulatory fluency. Familiarity with building secure, resilient systems under HIPAA and SOC 2., * Total Professional Experience: 5+ years building data-intensive software systems in production. * Data Engineering: 3+ years focused on data engineering, pipeline ownership, or data modeling, ideally in the healthcare or life sciences domain. * Cloud Infrastructure: 2+ years hands-on building and operating on Google Cloud, with demonstrated ownership of infrastructure decisions rather than consuming someone else's. * Healthcare Domain: 2+ years in HIPAA-regulated environments, with hands-on exposure to EMR integrations (Epic, Cerner) and healthcare data standards. * AI/ML Orchestration: 1+ years hands-on building with Large Language Models - agentic workflows, RAG, or autonomous tool use. * Data at Scale: Demonstrated experience managing structured (SQL, NoSQL) and unstructured data at a scale of millions of records, ensuring data integrity for downstream AI consumption., * Primary Requirement: Bachelor's degree in Computer Science, Software Engineering, Data Science, Health Informatics, or a related technical field. * Preferred: Master's degree or Ph.D. in Computer Science (AI/ML or distributed systems focus) or Biomedical Informatics. * Alternative Background: Equivalent professional experience - including a portfolio of significant open-source contributions or industry-recognized technical writing - will be considered. Bonus Points * Google Cloud Healthcare API. Direct experience with managed FHIR or DICOM stores. * Specialized clinical data. Direct experience with OMOP, DICOM, genomic data models, or longitudinal patient records. * Kubernetes. Experience running containerized workloads on Kubernetes. * AI data engineering. Experience with vector databases (Pinecone, Weaviate, pgvector) or graph databases to support RAG and agentic memory. * AWS. Experience with AWS services alongside Google Cloud Platform in a multi-cloud environment. * Advanced modeling techniques. Experience with Data Vault 2.0, Master Data Management, or comparable enterprise modeling methodologies. ## Description * Write the software on top. Build the TypeScript services and APIs that handle agent input and output and coordinate specialized agents, meeting the platform's performance and scalability demands. You are expected to be comfortable in the application codebase, not only in the data layer. * Own the infrastructure your platform runs on. Extend and operate the platform's Google Cloud footprint in Terraform - BigQuery datasets, Pub/Sub, Cloud SQL, Cloud Storage, Memorystore, Secret Manager, and the service-account and IAM model that governs access to clinical data. * Scale across hospital networks. Build for federated networks of hospitals: multi-tenancy, high availability, and performance across hybrid on-prem and cloud environments built for sensitive health-system integrations. * Guarantee ground truth. Implement automated solutions to monitor data quality and lineage with strict traceability back to source systems, ensuring "ground truth" for agentic evaluations. * Instrument for trust. Build the observability, error tracking, and human-in-the-loop checkpoints that make automated clinical evaluation transparent and debuggable. * Raise the standard around you. Partner with clinical, analytics, and platform engineering teams on data modeling standards, governance, and practices for maintaining data integrity in a HIPAA-regulated environment. How You Work We care about these as much as the technical checklist. * High ownership. You own what you build all the way into production - you care whether it stays up, you chase root causes instead of symptoms, and you do not treat the deploy boundary as the end of your responsibility. * Self-starter. The problem space is genuinely open. You are comfortable identifying the most valuable next thing and starting on it without a fully specified ticket, and you surface ambiguity early rather than stalling on it. * Collaborative. You work directly with clinical, analytics, and platform engineering partners. You write things down, you explain trade-offs to non-specialists, and you make the people around you faster. * Quick to add impact and value. You bias toward shipping something real and incremental early over long design cycles, and you look for the change that moves the platform now. Our Stack You will not have used all of this, and we do not expect you to have. It is here so you know what you would be working in. * Warehouse and transformation: BigQuery, dbt * Operational data stores: Cloud SQL (PostgreSQL), Memorystore (Redis), Cloud Storage * Messaging: Pub/Sub with dead-letter queues * Healthcare data: Google Cloud Healthcare API FHIR stores, HL7/FHIR, DICOM, Avro * Languages: Python for data pipelines and transforms; TypeScript on Node for platform services and APIs * Application frameworks: NestJS, TypeORM * Infrastructure: Terraform, Docker, Secret Manager, service-account and IAM-based access control * Decisioning: GoRules ZEN engine for versioned decision models * Cloud: primarily Google Cloud, with some AWS at the edges ## Related Videos - [Leveraging Real time data in FSIs](https://www.wearedevelopers.com/videos/806-leveraging-real-time-data-in-fsis) - [Reducing LLM Calls with Vector Search Patterns - Raphael De Lio (Redis)](https://www.wearedevelopers.com/videos/1714-reducing-llm-calls-with-vector-search-patterns-raphael-de-lio-redis) - [From event streaming to event sourcing 101](https://www.wearedevelopers.com/videos/91-from-event-streaming-to-event-sourcing-101) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Tomorrow's cloud data platforms - fully managed database-as-a-service (DBaaS)](https://www.wearedevelopers.com/videos/254-tomorrow-s-cloud-data-platforms-fully-managed-database-as-a-service-dbaas) - [Accelerating Authentication Architecture: Taking Passwordless to the Next Level](https://www.wearedevelopers.com/videos/733-accelerating-authentication-architecture-taking-passwordless-to-the-next-level) ## Related Articles - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Best US AI Conferences for CTOs in 2026: Build vs. Buy, Vendor Evaluation, and Peer Intelligence](https://www.wearedevelopers.com/magazine/736-best-us-ai-conferences-for-ctos-in-2026-build-vs-buy-vendor-evaluation-and-peer-intelligence) - [7 Cloud Computing Trends Coming in 2025 for Developers](https://www.wearedevelopers.com/magazine/412-7-cloud-computing-trends-coming-in-2025-for-developers)