Here's what you'll do
- Own the design, build, and optimization of star schemas, data vaults, operational data stores (ODS), and semantic models for analytical and operational workloads.
- Own performance tuning, query optimization, and monitoring across the semantic layer.
- Own and deliver trusted semantic models that fuel self-service analytics that address company KPIs and help influence better decisions through data driven insights.
- Serve as the primary analytics engineering point of contact for business units (Finance, Operations, Commercial, Maintenance, Safety, etc.), owning the translation of their data needs into technical solutions.
- Own the translation of business requirements into scalable, analytics-ready datasets aligned to KPIs, metrics definitions, and governance standards.
- Define, own, and maintain data modeling standards, glossary, lineage, documentation, and best practices.
- Use Python, R, SQL, and other programming languages for data cleaning, transformation, and analysis tasks
- Support security and compliance requirements such as access control, data masking, and regulatory reporting needs.
- Other duties and responsibilities as determined by leadership
- Achieve performance measures and adhere to established standards in conjunction with Breeze Aviation Group Values of Safety, Kindness, Integrity, Ingenuity, and Excellence
Here's what you'll need to be successful Minimum Qualifications
- Combination of experience and education will be considered
- Bachelor’s degree in Data Science, Information Systems, Computer Science, Statistics, Data Analytics, or a related field.
- 5+ years of experience in Analytics Engineering, Business Intelligence, or Data Engineering roles.
- Advanced SQL skills across major platforms (Snowflake, SQL Server, Oracle, Postgres, etc.).
- Strong experience building and maintaining semantic/analytical models, especially dimensional models, star schemas, and KPI frameworks.
- Experience with business intelligence tools (e.g., Power BI, Tableau, Looker)
- Strong analytical and problem-solving skills
- Knowledge of RESTful APIs and experience with tools like Postman or similar
- Familiarity with data warehousing concepts and ETL processes
- Comfortable using agentic AI tools and large language model (LLM) assistants to accelerate development, data modeling, and analysis workflows.
- Working knowledge of the Model Context Protocol (MCP) and connecting AI agents to data sources, tools, and internal systems
Preferred Qualifications
- Experience in the aviation industry
- Background in self-service analytics enablement and BI governance.
Perks of the Job
- Health, Vision and Dental
- Health Savings Account with Breeze Employee Match
- 401K with Breeze Employee Match
- PTO
- Travel on Breeze and other Airlines too!
Working at Breeze Airways is an exciting endeavor and a serious commitment to bring “The World’s Nicest Airline” to life. We work cross-functionally with truly awesome Team Members to deliver on our mission: “To make the world of travel simple, affordable, and convenient. Improving our guests travel experience using technology, ingenuity and kindness.”
Breeze is hiring- join us!
The Analytics Engineer owns the delivery of high-quality, analytics-ready data that powers enterprise reporting, operational insights, data science models, and strategic decision-making. This position owns the end-to-end design, build, and ongoing health of scalable semantic models within Snowflake, ensuring trusted, performant, and well‑governed data assets for use across platforms such as Power BI, Posit Connect, etc. The role serves as a bridge between data engineering and business analytics—translating complex operational, commercial, and financial data into curated datasets that enable self-service reporting and consistent KPI definitions.
Aviation industry experience is strongly preferred, as this role frequently partners with teams across the business to model data that supports regulatory reporting, operational performance, data science initiatives, and mission-critical airline metrics. The ideal candidate combines strong SQL and modeling expertise with the ability to engage stakeholders, define business requirements, take ownership of advancing the maturity of the enterprise semantic layer, and serve as a technical point of reference for the broader analytics team.