Healthcare Data Engineer Onsite in Nashville, TN

Healthcare Data Engineer – Real-Time & Cloud Platforms

Location :- Nashville, TN ( Onsite from day 1 )

Job Summary

As a Consulting Data Engineer, you will architect, build, and optimize the next generation of our real-time healthcare data platforms and event-driven architectures. You will play a foundational role in moving our clinical, operational, and financial data away from legacy batch processing toward high-throughput, near-zero-latency streaming pipelines.

In this role, you will champion modern data engineering practices, design highly scalable cloud-native pipelines, and mentor a growing team of engineers. You will bridge the gap between complex electronic health records (EHR) and data consumer-enabling data scientists, software engineers, and clinical analysts to securely access, stream, and query compliant data at scale to improve patient outcomes and operational efficiency.

Core Responsibilities

• Architect Real-Time Pipelines: Design, implement, and monitor high-throughput, fault-tolerant streaming pipelines that ingest and process millions of concurrent healthcare data events (e.g., live telemetry, HL7 feeds, ADT messages).

• Scale Google Cloud Infrastructure: Build and maintain scalable, secure, and cost-effective cloud data infrastructure using Google Cloud healthcare-native tools and Infrastructure as Code (IaC).

• Enforce Healthcare Compliance & Security: Uphold strict data security protocols (HIPAA, HITRUST, SOC2) by building robust data lakehouse architectures that enforce PHI/PII masking, role-based access control (RBAC), and encryption at rest and in transit.

• Enable Self-Service Data & Interoperability: Build out streaming data ecosystems that support modern healthcare interoperability, managing real-time data catalogs and schema registries.

• Promote Code Excellence: Write modular, highly reusable, and performance-tested code; lead code reviews and establish standardized CI/CD automated deployment workflows.

• Optimize Data Reliability: Set and champion strict SLAs for clinical data delivery, establish automated data-quality testing, and implement proactive system monitoring and alerting.

• Collaborate & Mentor: Work closely with Data Architects, Clinical Product Managers, and AI/ML teams to translate healthcare requirements into technical blueprints while mentoring mid-level engineers.

Required Qualifications

Experience & Education

• 13+ years of experience in dedicated data engineering, data architecture, or distributed systems software engineering roles.

• 4+ years of experience specifically within the healthcare domain (payer, provider, digital health, or health tech).

• Google Cloud Certified Professional Data Engineer (or equivalent proven expert-level architecture experience on Google Cloud Platform).

True Streaming Focus

• Advanced Event-Driven Architecture: Mastery of high-volume streaming technologies including Apache Kafka and Google Cloud Pub/Sub for decoupled, real-time message routing.

• Stream Processing: Deep hands-on experience with Apache Flink or Google Cloud Dataflow (Apache Beam) for real-time streaming, complex event processing (CEP), windowing, and streaming data transformations.

Healthcare-Specific Tech Stack

• Interoperability Standards: Deep expertise working with FHIR (Fast Healthcare Interoperability Resources) JSON structures, HL7 v2/v3 messaging, and DICOM imaging standard streams.

• Google Cloud Platform Healthcare Tools: Hands-on experience implementing and scaling the Google Cloud Healthcare API (specifically FHIR stores and HL7v2 stores).

• EHR Integration: Experience integrating streaming pipelines with major EHR systems (such as Epic, Cerner, or Meditech).

Cloud Ecosystem & Modern Tooling

• Google Cloud Ecosystem: Technical mastery of BigQuery (including partitioning, clustering, and streaming inserts), Cloud Storage, Dataproc, and Cloud Composer (Apache Airflow).

• Programming Languages: Proficiency in Python, Scala, or Java, with a strong focus on writing optimized code for distributed computing platforms (like Spark or Beam).

• Modern Data Stack & Modeling: Mastery of modern analytics engineering frameworks (such as dbt), dimensional data modeling, and lakehouse architectures (e.g., Iceberg, Delta Lake).

• DevOps & Automation: Robust experience building automated CI/CD pipelines (GitHub Actions, GitLab CI) and managing infrastructure using Terraform.