Healthcare Data Engineer

Description
Data Engineers serve a unique and critical role in daily operations at Wider Circle. Customer and program data are the bedrock of our business, and Data Engineering is responsible for building and maintaining the systems that power analytics, reporting, and product intelligence.

We are looking for a hands-on, impact-oriented Data Engineer to build and maintain reliable data pipelines, modernize legacy workflows, and support analytics and machine learning use cases. You will primarily work within Amazon Web Services (AWS), supporting Amazon Redshift, Python-based pipelines, and lightweight AI/LLM integrations.

This role requires someone who ships production code, improves system reliability, and partners closely with analytics and data science to ensure data is trustworthy, well-modeled, and actionable. You will join a talented, fully remote Data Science, Engineering & Analytics team that handles customer data processing, automation, product analytics, complex integrations, and data-driven innovation.

Responsibilities

Core Data Engineering

Build and maintain scalable ETL/ELT pipelines using Python (pandas) and SQL
Ingest data from Amazon S3, APIs, Salesforce, and internal systems
Write performant SQL in Amazon Redshift (DDL, DML, stored procedures)
Manage schemas, views, permissions, and table evolution safely
Debug production data issues and performance bottlenecks
Ensure data quality, freshness, lineage, and observability
Document pipelines and datasets clearly
Ensure appropriate data safeguards for sensitive and regulated data, including PHI and PII

Orchestration & Automation

Migrate legacy cron-based workflows to more robust orchestration frameworks
Implement idempotent, retry-safe, production-ready jobs
Improve reliability and monitoring of existing pipelines
Use Git for version control and CI-friendly development practices

Analytics & Modeling Support

Partner with Analytics and Data Science to provide clean, modeled datasets
Support BI tools, reporting workflows, and Google Sheets integrations
Ensure internal SLAs for data quality and delivery frequency are met
Provide expert support for complex data integration challenges
AI / LLM Integration

Build lightweight AI-powered utilities (e.g., metadata extraction, SQL generation, anomaly explanation)
Integrate LLM APIs into existing data workflows
Focus on practical augmentation that saves analyst and engineer time

What Success Looks Like

Data pipelines are reliable, observable, and well-documented
Redshift schemas are clean, performant, and well-managed
Analysts and stakeholders trust the data
AI-powered tools meaningfully reduce engineering or analyst workload
Internal SLAs for data delivery and quality are consistently met

Requirements
3–6 years of experience in data engineering or analytics engineering
Strong Python skills (dataframes, file I/O, APIs)
Strong SQL skills, including warehouse-specific optimization
Hands-on experience with AWS (S3, IAM, Redshift)
Experience using APIs for data ingestion and system integration
Experience with Git and collaborative development workflows
Comfortable working with imperfect data and legacy systems

Preferred Qualifications

Experience replacing cron with modern orchestration tools (e.g., Airflow or similar)
Experience with Salesforce API integrations
Familiarity with Google Drive / Google Sheets APIs
Exposure to LLM APIs (OpenAI, Anthropic, etc.)
Experience working with healthcare data (claims, eligibility, CDAs/HRAs)
Experience partnering with Data Scientists to productionalize models
Experience with tools such as Matillion, Mulesoft, or similar
Benefits