Analytics Engineer, Data Platform

About the role

This role sits within a small, growing Analytics Engineering team and is an opportunity to shape the platform from the ground up.

Responsibilities

• Design, build, and maintain dbt models that transform raw clinical, pharmacy, billing, and care operations data into clean, reliable, domain-specific data marts.
• Partner with Data and Software Engineering on ETL pipeline design, data ingestion, and raw-to-staging transformations by ensuring data arrives in a form that AE can work with.
• Develop and own the semantic layer in Omni by defining governed metric definitions, curated datasets, and self-service data products that analysts and stakeholders can consume directly.
• Build a thorough testing suite across the data platform: schema tests, data quality checks, anomaly detection, and SLA monitoring to ensure stakeholders can trust what they see.
• Implement and maintain data governance practices including lineage documentation, cataloging, access control, and column-level documentation in dbt.
• Become a domain expert in your assigned area (pharmacy operations, billing, or care operations) by deeply understanding the business logic and translating it into accurate, scalable data models.
• Work closely with analysts to understand their data needs, accelerate their workflows, and reduce time spent on ad hoc data prep — enabling them to focus on higher-order analysis and strategy.
• Contribute to platform-level decisions: warehouse organization, modeling conventions, CI/CD for dbt, and tooling standards across the AE team.
• Proactively identify data quality issues, gaps in coverage, and opportunities to improve the reliability and usability of the data platform.

Requirements

• Strong SQL proficiency: comfortable writing complex queries, CTEs, window functions, and performance-optimized transformations across large datasets.
• Hands-on experience with dbt (Core or Cloud): you understand the modeling layer, ref() dependencies, tests, macros, and how to structure a well-organized dbt project.
• Solid understanding of data warehouse concepts: dimensional modeling, mart layers, slowly changing dimensions, and how to think about the staging / intermediate / mart separation.
• Experience working with ETL/ELT pipelines and partnering with data or software engineers on data ingestion.
• Comfort with the command line: run scripts, manage files, and troubleshoot basic shell operations. You don't need to be a sysadmin, but you're not afraid of a terminal.
• Strong analytical instincts: able to interrogate data, identify anomalies, trace root causes, and communicate findings clearly to both technical and non-technical audiences.
• Comfort working in ambiguous, fast-moving environments with competing priorities.

Qualifications

• Bachelor's degree in Computer Science, Economics, Engineering, Mathematics, or a related quantitative field, or equivalent practical experience.
• Experience with a semantic layer or BI tool such as Omni, Looker, Metabase, or similar — especially defining metrics, dimensions, and governed data products.
• Familiarity with healthcare data: clinical, pharmacy, billing, or claims data from EHRs, TPAs, or pharmacy operating systems.
• Experience with data quality frameworks, testing strategies, or anomaly detection in a production data environment.
• Exposure to data governance tooling: data catalogs, lineage tracking, or column-level documentation.
• Python or another scripting language for data tasks or pipeline work.

Benefits

• Medical
• Dental
• Vision Insurance
• Company
• Paid time off
• Short- and Long-Term Disability

Pay

• Competitive salary

Schedule

• Full-time