Key skills
AirflowAmazon RedshiftApacheAWSBigQueryCloudPythonSQLAIClaudeAgenticData WarehousingAnalyticsBusiness IntelligenceSnowflakeRedshiftApache AirflowdbtGitVersion ControlCI/CDCommunication
About this role
Role Overview
- Apply hands on analytics and data expertise to solve complex, fast moving financial and operational problems in the health tech space
- Design, develop and maintain scalable, analytics ready data models (primarily in dbt) that power a PBM and care navigation data ecosystem that support client reporting, performance analytics, operational analysis and self-service business intelligence.
- Translate complex pharmacy benefits and healthcare navigation workflows, business rules and operational processes into transparent, maintainable, and auditable data models.
- Partner closely with data and analytics engineers, data analysts and business stakeholders to deliver reliable data products that can be leveraged across multiple use cases and continuously optimize data models and warehouse performance to support large-scale PBM/care navigation datasets and growing business needs.
- Implement automated data quality checks, testing frameworks and reconciliation processes to ensure data reliability.
- Establish documentation standards, lineage and analytics engineering guardrails that promote transparency and auditability.
- Contribute to engineering best practices including version control, CI/CD, incremental models, code reviews, and observability.
- Contribute to data governance by establishing modeling standards, documentation and guardrails that support auditability, explainability and long term maintainability.
- Build data foundations that enable future agentic analytics engineering use cases, including self-service analytics and AI-assisted insight generation.
- Stay informed on emerging technologies and identify opportunities to incorporate AI into analytics engineering workflows.
Requirements
- 3+ years of experience in analytics engineering, data modeling, data warehousing or a related field.
- Strong experience with dbt and modern analytics engineering best practices (required).
- Advanced SQL proficiency and experience developing data transformations and workflows using python.
- Strong understanding of dimensional modeling, medallion architecture, semantic modeling and scalable data architecture principles.
- Experience building and maintaining production-grade data models, data quality tests, and documentation.
- Experience working with cloud data warehouses such as Amazon Redshift, Snowflake, or BigQuery.
- Experience orchestrating and monitoring data pipelines using tools such as Apache Airflow., Dagster etc.
- Experience working within cloud environments, preferably AWS.
- Familiarity with software engineering practices including Git, CI/CD, pull request reviews, testing, and deployment workflows.
- Interest or experience in leveraging AI-enabled development workflows and analytics tooling, including experience with or willingness to learn technologies such as claude, claude skills, Model Context Protocol (MCP) and AI assisted engineering practices.
- Strong problem-solving and analytical skills with a focus on building scalable, maintainable data solutions.
- Effective communication skills and the ability to collaborate with both technical and non-technical stakeholders.
Tech Stack
- Airflow
- Amazon Redshift
- Apache
- AWS
- BigQuery
- Cloud
- Python
- SQL
Benefits
- Compensation offered will be determined by geographic location, experience, and qualifications.