Data Integration Engineer II

mPulse is a conversational AI and healthcare member engagement company serving health plans and improving health outcomes. The Data Integration Engineer II will design, develop, maintain, and optimize scalable data pipelines supporting analytics, reporting, and product capabilities, while collaborating with cross-functional teams to ensure accurate, reliable, and accessible data.

Responsibilities:

• Design, develop, and maintain scalable data integration pipelines (ETL/ELT) to support data ingestion, transformation, cleansing, curation, and unification across multiple data sources
• Develop and support end-to-end data pipeline components, including ingestion, validation, transformation, cleansing, and curated data layer development
• Monitor, maintain, and optimize production data pipelines to ensure reliability, performance, and successful execution of scheduled workflows
• Create and maintain comprehensive technical documentation for data pipelines, workflows, and data models
• Develop tools and frameworks to support automated data profiling, data quality monitoring, and unit testing to ensure high-quality and reliable data assets
• Collaborate with implementation teams to identify, investigate, and resolve data anomalies during data onboarding and integration processes
• Partner with product engineering teams to ensure accurate data capture and alignment with application data specifications and business requirements
• Provide data-related support to analytics and customer success teams, assisting with troubleshooting, reporting needs, and client data inquiries

Requirements:

• Bachelor's or master's degree in computer science, Engineering, or a related technical field, or equivalent practical experience
• Minimum of 3 years of professional experience in data engineering, data integration, or a related role
• Strong proficiency in SQL, including complex querying, data transformation, and query performance optimization
• Experience working with cloud-based data platforms and services, particularly within AWS (e.g., S3, Secrets Manager/Vault, DMS, or similar services)
• Hands-on experience with modern data warehousing platforms, such as Snowflake, PostgreSQL, Amazon Redshift, or Microsoft SQL Server
• Experience developing, debugging, and maintaining workflow orchestration pipelines using Apache Airflow, including DAG development and operational support
• Experience using dbt (data build tool) to develop, test, and manage modular SQL-based data transformation models within modern data warehouse environments
• Experience with version control systems and collaborative development workflows, using tools such as GitHub or Bitbucket
• Proficiency in Python, particularly for data manipulation, automation, and integration tasks
• Familiarity with CI/CD practices and automation tools, such as Jenkins or GitHub Actions
• Strong written and verbal communication skills, with the ability to collaborate effectively across technical and non-technical teams
• Experience supporting data quality monitoring, data observability, or automated validation frameworks
• Familiarity with data science or machine learning workflows from a data engineering perspective
• Experience working with healthcare-related datasets, such as claims, clinical, or regulatory data