Data Engineer II

mPulse is the leader in conversational AI and member engagement for healthcare. They are seeking a Data Engineer II to design, develop, and maintain scalable data pipelines that support analytics, product, and AI/ML initiatives in a healthcare environment.

Responsibilities:

• 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 requirements 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

Requirements:

• 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 proficiency in SQL, including complex querying, data transformation, and performance optimization
• Proficiency in Python for data processing, automation, and integration tasks
• Experience designing and building data pipelines (ETL/ELT) to support data ingestion, transformation, and delivery
• Hands-on experience with modern data warehousing platforms, such as Snowflake, PostgreSQL, Amazon Redshift, or Microsoft SQL Server
• Experience with workflow orchestration tools, particularly Apache Airflow (DAG development, debugging, and maintenance)
• Experience using dbt (data build tool) to develop, test, and manage modular data transformation workflows
• Experience working with cloud platforms, particularly AWS (e.g., S3, RDS, Lambda, Glue, DMS)
• Experience with version control systems and collaborative development workflows, such as GitHub or Bitbucket
• Familiarity with CI/CD practices and tools, such as Jenkins or GitHub Actions
• Experience supporting data quality initiatives, including data profiling, validation, or monitoring frameworks
• Familiarity with data modeling and data warehousing concepts, including dimensional modeling
• Strong analytical and problem-solving skills, with the ability to work with complex datasets and identify root causes
• Strong written and verbal communication skills, with the ability to effectively communicate technical concepts to both technical and non-technical stakeholders
• Ability to collaborate effectively in cross-functional environments, working with engineering, product, analytics, and operations teams
• Strong attention to detail and commitment to data accuracy, consistency, and quality
• Demonstrated ability to manage multiple priorities and deliver high-quality work in a fast-paced environment
• Demonstrates self-awareness, with the ability to recognize and communicate individual strengths and areas for growth
• Shows a strong willingness to learn, adapt, and support team members, contributing to a collaborative and positive team environment
• Effectively communicates progress, priorities, and status updates to both internal and external stakeholders in a clear and timely manner
• Exposure to analytics, reporting, or data visualization tools (e.g., Tableau, Looker) is a plus
• Experience working with healthcare data, including claims or clinical datasets, is a plus
• Familiarity with data science or machine learning workflows from a data engineering perspective is a plus