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mPulse Mobile is seeking a Data Engineer II to design, develop, and maintain scalable data pipelines and platform capabilities supporting analytics, product, and AI/ML initiatives. The role focuses on processing high-volume, high-velocity healthcare data to enable insights and data-driven solutions for a growing client base.
Key responsibilities include:
- Design and develop scalable ETL/ELT data pipelines for ingestion, transformation, and delivery of 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 with automated data profiling, validation, and testing
- Monitor and optimize pipeline performance, reliability, and efficiency in production
- Analyze complex data issues, identify root causes, and implement scalable solutions
- Collaborate cross-functionally with Data Operations, Product, Analytics, and Data Science teams
- Partner with clinical and analytics teams to operationalize data-driven insights and reporting
- Contribute to documentation of data pipelines, data models, and engineering processes
You will work in a healthcare data environment, supporting both operational and analytical needs. The ideal candidate is detail-oriented, passionate about solving complex data challenges, and comfortable working with high-volume datasets in a fast-paced environment.
REQUIREMENTS:
- Bachelor's or Master's degree in Computer Science, Engineering, or related technical field, or equivalent practical experience
- 3+ years of professional experience in data engineering or related field
- Strong proficiency in SQL, including complex querying, data transformation, and performance optimization
- Proficiency in Python for data processing, automation, and integration
- Experience designing and building ETL/ELT data pipelines
- Hands-on experience with modern data warehousing platforms (Snowflake, PostgreSQL, Amazon Redshift, or Microsoft SQL Server)
- Experience with Apache Airflow (DAG development, debugging, maintenance)
- Experience with dbt (data build tool) for data transformation workflows
- Experience with AWS cloud platforms (S3, RDS, Lambda, Glue, DMS)
- Experience with version control systems (GitHub, Bitbucket)
- Familiarity with CI/CD practices and tools (Jenkins, GitHub Actions)
- Experience supporting data quality initiatives (profiling, validation, monitoring)
- Familiarity with data modeling and data warehousing concepts
- Strong analytical and problem-solving skills
- Strong written and verbal communication skills
- Ability to collaborate effectively in cross-functional environments
- Strong attention to detail and commitment to data accuracy
- Ability to manage multiple priorities in a fast-paced environment
PLUS (preferred):
- Exposure to analytics, reporting, or data visualization tools (Tableau, Looker)
- Experience working with healthcare data (claims or clinical datasets)
- Familiarity with data science or machine learning workflows from a data engineering perspective
