Data Engineer

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.

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 and enable investigative analysis
• Work with modern data tools to accelerate analysis and data discovery
• 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

Requirements

• Strong experience in data engineering and ETL/ELT development
• Proficiency in SQL (advanced querying, optimization)
• Experience with data transformation frameworks
• Experience working with large-scale datasets in enterprise environments
• Experience with modern data platforms such as Snowflake or cloud data warehouses
• Familiarity with distributed data processing frameworks and orchestration tools
• Strong debugging and problem-solving skills
• Ability to work in an offshore delivery model supporting global teams

Skills

• SQL
• Snowflake
• ETL/ELT
• Python
• Data Orchestration

Nice to Have

• 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