Role Overview
We are seeking two offshore Data Engineers to support ongoing enterprise data engineering and advanced analytics initiatives. These roles will function as long-term, steady-state support resources, contributing to data platform operations, ETL development, and emerging AI-enabled analytics use cases.
The ideal candidates will bring strong foundational data engineering expertise while being comfortable working in a modern analytics ecosystem that includes advanced tooling for data exploration and productivity acceleration.
Key Responsibilities
Design, develop, and maintain ETL/ELT pipelines to support enterprise data processing and analytics
Perform data ingestion, transformation, and integration across multiple datasets and domains
Support data engineering operations for ongoing analytics and reporting workflows
Collaborate with business and analytics teams to:
Explore datasets
Enable investigative analysis (e.g., fraud, waste, and abuse use cases)
Work with modern data tools to accelerate analysis and data discovery, while ensuring outputs are validated for accuracy
Ensure data quality, reliability, and performance of pipelines and datasets
Participate in continuous enhancement of data models and workflows
Support production and non-production environments as part of an ongoing support
Required Skills & Experience
Core Data Engineering
Strong experience in data engineering and ETL/ELT development
Proficiency in:
SQL (advanced querying, optimization)
Data transformation frameworks
Experience working with large-scale datasets in enterprise environments
Platform & Tools (Representative)
Experience with modern data platforms such as:
Snowflake / cloud data warehouses
Distributed data processing frameworks
Familiarity with data pipeline orchestration tools
Analytics & Data Exploration
Ability to support data analysis and investigative workflows
Exposure to tools that enhance productivity in data analysis (e.g., code-assisted or AI-enabled exploration)
Strong understanding of data validation and result verification, especially when using automated or AI-assisted outputs
Engineering Practices
Strong debugging and problem-solving skills
Experience with data quality checks and validation frameworks
Ability to work in an offshore delivery model supporting global teams
Preferred Qualifications
Experience supporting fraud, waste, and abuse (FWA) or similar investigative analytics use cases
Familiarity with AI-assisted data engineering or analytics tools
Exposure to healthcare data domains (claims, providers, clinical, etc.)
Experience working in long-duration support / managed services environments
Work Location: Remote
