Are you passionate about building scalable analytics platforms, enabling self-service data access, and transforming raw data into trusted business insights?
We are seeking an Analytics Engineer to design, build, and maintain modern analytics infrastructure that powers reporting, data science, machine learning, and operational decision-making. This role serves as the bridge between Data Engineering, Analytics, and Data Science teams, ensuring business data is reliable, accessible, well-modeled, and production-ready.
The ideal candidate combines expertise in SQL, dbt, Python, BigQuery, API development, data modeling, and cloud analytics platforms, with experience working with healthcare claims data and building scalable ELT pipelines.

Key Responsibilities
Analytics Engineering & Data Modeling
Design and maintain scalable analytics data models that support:
Business Intelligence
Reporting
Self-Service Analytics
Machine Learning Features
Operational Data Products
Translate business requirements into efficient, reusable data structures.
Define and standardize enterprise metrics and KPIs.
Build semantic layers that improve data consistency across teams.
ELT/ETL Pipeline Development
Design, develop, and maintain analytics transformation pipelines.
Build scalable ELT workflows using:
dbt Core
SQL
Python
BigQuery
Create modular and reusable transformation frameworks.
Improve maintainability and scalability of data pipelines.
Ensure timely and reliable delivery of analytical datasets.
SQL & Data Transformation Engineering
Develop advanced SQL models to support analytics and reporting requirements.
Build Python-based transformation processes and testing frameworks.
Optimize transformations for performance and scalability.
Support feature engineering workflows for machine learning initiatives.
Ensure code quality through testing and version-controlled development practices.
Healthcare Claims Data Analytics
Work with healthcare claims datasets and related business domains.
Develop scalable data models supporting:
Claims Processing
Financial Reporting
Utilization Analysis
Member Analytics
Provider Analytics
Ensure healthcare data integrity and consistency across platforms.
Support regulatory and operational reporting requirements.
Data Quality, Governance & Observability
Implement comprehensive data quality controls.
Develop and maintain:
Data Validation Rules
Automated Testing
Monitoring Frameworks
Data Lineage Documentation
Ensure analytics outputs meet SLA expectations.
Support data governance initiatives and quality assurance processes.
Improve trust and transparency across analytical platforms.
API Development & Data Services
Build and maintain APIs that expose curated datasets and analytical assets.
Design and support:
REST APIs
Data Access Services
Feature Delivery Services
Internal Data Products
Integrate data services into downstream business applications.
Enable secure and scalable access to analytical resources.
LLM & AI Integration
Integrate Large Language Model (LLM) APIs into analytics and product workflows.
Support AI-enabled reporting and analytics initiatives.
Build workflows that leverage:
LLM APIs
AI Enrichment Services
Intelligent Data Products
Collaborate with ML teams on AI-powered solutions.
Cloud Platform Engineering
Develop and operate analytics solutions on Google Cloud Platform (GCP).
Utilize:
BigQuery
Cloud Run
Serverless Infrastructure
Cloud-Based Data Services
Support scalable, cloud-native analytics architectures.
Ensure reliable deployment and monitoring of analytics services.
Performance Optimization & Cost Management
Optimize BigQuery performance through:
Query Tuning
Partitioning
Clustering
Efficient Data Modeling
Monitor cloud resource utilization and costs.
Improve operational efficiency while maintaining performance standards.
Implement cost-effective analytics solutions.
Documentation & Knowledge Sharing
Maintain documentation for:
Data Models
Transformation Logic
APIs
Operational Processes
Runbooks
Mentor team members on:
dbt Best Practices
SQL Optimization
Analytics Engineering Principles
Promote engineering excellence and analytics standards.
Cross-Functional Collaboration
Partner closely with:
Product Teams
Data Scientists
Data Analysts
Machine Learning Engineers
Software Engineers
Align analytical solutions with business objectives.
Support operationalization of analytics and machine learning workflows.
Drive adoption of self-service analytics capabilities.