Data Integration Engineer II

Summary

mPulse is a healthcare conversational AI and member engagement company serving health plans and improving health outcomes. The Data Integration Engineer II will design, develop, maintain, and optimize scalable data pipelines that support analytics, reporting, and product capabilities. The role also focuses on data quality, documentation, troubleshooting, and collaboration across engineering, implementation, analytics, and customer success teams.

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

Skills

• 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

Qualifications

Must Haves

• 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

Nice to Haves

• 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

Benefits

• Remote-First & Flexible PTO — work from wherever you're most productive
• 100% Company-Paid Employee Coverage - Medical, dental, and vision plans with a 100% company-paid employee-only option, plus company contributions toward dependent coverage and company-paid life and disability insurance.
• 401(k) + 4% Match — with financial advisors to help you plan
• 6 Weeks Parental Leave
• 30-60-90 day plans and frequent training
• Culture of Recognition — peer-to-peer bonuses & team celebrations