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# Principal Data Architect (Data Science)

- **Company:** EGEN Solutions Inc
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $180,000.0 - $240,000.0
- **Contract:** Permanent contract
- **Skills:** Automation of Tests, Cloud Computing, Databases, Continuous Integration, Data Architecture, Extract Transform Load (ETL), Dicom, Python (Programming Language), NoSQL, SQL Databases, Unstructured Data, Data Processing, Cloud Platform System, Fast Healthcare Interoperability Resources, Apache Spark, Health Level Seven International, Docker
- **Published:** October 14, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=5d32612860dd52bc

## About the Role

* Experience: 6+ years working across data science and machine learning (for the Lead level).
 * Core Languages: Expert-level SQL and Python.
 * Data Types: Hands-on work with both structured and unstructured data, such as tables, text, images, and video.
 * Databases: Experience with both relational (SQL) and non-relational (NoSQL) databases.
 * Data Processing: Familiarity with ETL and data processing tools like Apache Spark.
 * Visualization and Monitoring: Experience using tools to visualize data and monitor its health.
 * Engineering Practices: Comfort with modern software practices like automated testing and CI/CD.
 * Containers: Experience with Docker.
 * Cloud: Experience deploying to GCP or another cloud platform., * Consulting or Professional Services Background
 * Domain Knowledge of Healthcare Data
 * Knowledge of Healthcare Protocols such as HL7, FHIR, DICOM

## Description

* Data Ingestion: Design and build the pipelines that bring data in for feature engineering and machine learning.
 * Feature Engineering: Use your knowledge of healthcare data, statistics, and data science to create features with real predictive power.
 * Feature Extraction and Selection: Use techniques like clustering, text analytics, and PCA to find the most useful signals while preserving the meaning in the original data, so models stay accurate and scale well.
 * Exploratory Data Analysis: Explore the data to find patterns, trends, and relationships, and use what you learn to match features with the right modeling approach.
 * Model Development: Experiment with statistical and machine learning methods to build, tune, and optimize predictive models that fit the use case.
 * Deployment: Ship production code that follows engineering best practices.
 * Documentation and Communication: Document your work and share your approach, insights, and solutions with technical and non-technical audiences.
 * Monitoring: Build and maintain the pipelines and processes that keep our enterprise data science products running smoothly.
 * Quality and Integrity: Keep data and outputs accurate and reliable from start to finish.
 * Collaboration: Partner with stakeholders, data scientists, data engineers, and product managers to build and deliver great data products.