Data Engineer II

Job summary

About mPulse: mPulse is the leader in conversational AI and member engagement for healthcare. We partner with the nation's most innovative health plans — Medicare Advantage, Medicaid, and Commercial — to engage members in conversations that close care gaps, improve Stars ratings, and drive better health outcomes.

Qualifications

• The ideal candidate brings strong experience in SQL, Python, dbt, and Airflow, along with a solid foundation in data warehousing and a passion for solving complex data challenges in a healthcare environment.
• and deliver high-quality data solutions.
• Partner with clinical and analytics teams to operationalize data-driven insights and reporting solutions.
• Contribute to documentation of data pipelines, data models, and engineering processes to support maintainability and knowledge sharing.
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
• 3+ years of professional experience in data engineering or a related field.
• Strong analytical and problem-solving
• and individual
• without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other characteristic protected by applicable law.

Responsibilities

• In this role, you will design, develop, and maintain scalable data pipelines and platform capabilities that support our analytics, product, and AI/ML initiatives.
• You will work cross-functionally with Data Operations, Product, Analytics, and Data Science teams to ensure data is reliable, performant, and aligned with business needs.
• Design, develop, and maintain scalable data pipelines (ETL/ELT) to support ingestion, transformation, and delivery of high-volume healthcare data.
• Write, optimize, and maintain complex SQL queries for data transformation, validation, and performance tuning.
• Develop and manage workflow orchestration using Apache Airflow, including DAG creation, monitoring, and troubleshooting.
• Enhance and scale data platform capabilities to support analytics, product features, and AI/ML use cases.
• Build and maintain data quality frameworks, including automated data profiling, validation, and testing processes.
• Monitor and optimize pipeline performance, reliability, and efficiency in production environments.
• Analyze complex data issues, identify root causes, and implement scalable solutions, clearly communicating findings to both technical and non-technical stakeholders.
• Collaborate with cross-functional teams (Data Operations, Product, Analytics, Data Science) to gather

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