Analytics Engineer, Unity

This is a fully remote position, open to applicants in Florida.

📋 Description

• Create, develop, and sustain scalable analytics-ready data models, mainly using dbt, to support PBM and care navigation functions.

• Convert intricate healthcare data into reliable datasets for operational reporting, client insights, financial analysis, and strategic decision-making.

• Convert pharmacy benefits and healthcare navigation workflows, business rules, and operational processes into clear, maintainable, and auditable data models.

• Collaborate with data and analytics engineers, data analysts, and business stakeholders to deliver dependable and reusable data products.

• Enhance the performance of data models and warehouses for large-scale PBM and care navigation datasets.

• Implement automated data quality checks, testing frameworks, and reconciliation methods.

• Set documentation standards, data lineage, and analytics engineering guidelines.

• Participate in version control, CI/CD, incremental models, code reviews, and observability best practices.

• Contribute to data governance and modeling standards.

• Establish data foundations for self-service analytics, AI-driven insight generation, and future agentic analytics engineering applications.

• Keep abreast of emerging technologies and identify opportunities to integrate AI into analytics engineering workflows.

⛳️ Requirements

• A minimum of 3 years of experience in analytics engineering, data modeling, data warehousing, or a related discipline.

• Extensive experience with dbt (core or cloud) and modern analytics engineering best practices (mandatory).

• Advanced proficiency in SQL.

• Experience in developing data transformations and workflows using Python.

• Strong knowledge of dimensional modeling, medallion architecture, semantic modeling, and scalable data architecture concepts.

• Proven experience in building and maintaining production-grade data models, data quality tests, and documentation.

• Familiarity with cloud data warehouses such as Amazon Redshift, Snowflake, or BigQuery.

• Experience in orchestrating and monitoring data pipelines using tools like Apache Airflow or Dagster.

• Experience working within cloud environments, preferably AWS.

• Knowledge of Git, CI/CD, pull request reviews, testing, and deployment workflows.

• Interest or experience in AI-enhanced development workflows and analytics tools, including Claude, Claude Skills, Model Context Protocol (MCP), and AI-assisted engineering methods.

• Excellent problem-solving and analytical abilities.

• Strong communication skills and the capability to collaborate with both technical and non-technical stakeholders.

🏝️ Benefits

• Bonus

• Equity