Sr. Analytics Engineer

About the Role

We are hiring a Senior Analytics Engineer to join our unified, high-velocity Analytics Engineering pod. In this role you will partner with a talented team of engineers to co-own, scale, and optimize the "Transform" layer of our data ecosystem across modern data platforms.

This is a core technical engineering role. You will balance deep code execution in SQL and dbt with strong cross-functional stewardship, translating complex business processes into trusted, production-grade data models. The ideal candidate will bring a strategic mindset, excellent code hygiene, and the professional backbone required to manage competing technical priorities while navigating highly regulated data systems.

What You’ll Do

Data Modeling & Architecture: Design, build, and optimize modular dbt pipelines and performant technical schemas, setting the team standard for version control, unit testing, and velocity.

Cross-Functional Translation: Act as a critical technical bridge between Analytics, Operations, and Data Engineering to transform ambiguous product requirements into robust data assets.

Platform Optimization: Diagnose performance bottlenecks to slash query latencies and lower warehouse compute costs across our modern data platforms, specifically aiding our strategic migration toward Snowflake.

Team Stewardship: Elevate the entire pod’s engineering culture by participating in rigorous peer code reviews, refactoring legacy technical debt, and enforcing strict data security standards up to the point of delivery.

Culture Multiplier: Actively mentor and pair with junior peers to unblock complex bugs, optimize engineering workflows, and natively adopt AI-assisted development tools to accelerate test generation and legacy code discovery.

Required Qualifications

4+ years of professional experience in analytics engineering, data engineering, or highly technical data analytics roles.

Expert-level SQL proficiency and deep data modeling experience, with a proven ability to write performant queries and understand the architectural trade-offs of database design decisions.

2+ years of dedicated, hands-on experience building, deploying, and maintaining modular data pipelines using dbt in a production environment.

Proven experience designing schemas and optimizing query performance on modern cloud data platforms (such as Snowflake, Redshift, or BigQuery).

Absolute comfort with production git workflows (branching, merging, pull request reviews) and automated data quality testing practices.

Experience working in a cross-functional role partnering directly with analytics, operations, or business teams, where strong requirement-gathering and the ability to translate complex data structures into clear documentation are crucial.

Preferred Qualifications

While having the preferred qualifications enhances your candidacy, having all of them is not mandatory. We encourage all interested applicants to apply, even those who may not meet every preferred requirement.

Prior experience working with HIPAA-compliant data environments, electronic health records (EHR), digital health operations, or similar highly regulated industries.

Hands-on experience or deep familiarity with Snowflake architectures and running data migrations to Snowflake environments.

Active experience leveraging AI-assisted development tools (like GitHub Copilot, ChatGPT, Cursor, or Claude Code) to accelerate code generation, decipher legacy warehouse logic, and draft robust dbt test suites.

Experience with Fivetran or modern ingestion tools to deeply understand upstream data structures and better collaborate with the core data engineering pod.

A proactive approach to data quality, testing, and alert frameworks (such as dbt test packages) and building practical alerting workflows rather than just waiting for stakeholders to report broken data.

Experience managing changing business processes or schema drift when underlying logic or systems pivot unexpectedly.

Basic Python proficiency providing an extra layer of technical agility when collaborating on advanced pipeline orchestration or tooling.

Skills Required
4+ years professional experience in analytics engineering, data engineering, or highly technical data analytics roles
Expert-level SQL proficiency and deep data modeling experience
2+ years hands-on experience building, deploying, and maintaining modular dbt pipelines in production
Experience designing schemas and optimizing query performance on cloud data platforms (Snowflake, Redshift, or BigQuery)
Comfort with production git workflows (branching, merging, pull request reviews) and automated data quality testing practices
Experience partnering cross-functionally with analytics, operations, or business teams to gather requirements and document data assets
Prior experience working with HIPAA-compliant data, EHRs, or regulated health data environments
Hands-on experience or deep familiarity with Snowflake architectures and running migrations to Snowflake
Experience using AI-assisted development tools (GitHub Copilot, ChatGPT, Cursor, Claude Code) for code generation and test drafting
Experience with Fivetran or other modern ingestion tools to understand upstream data structures
Proactive approach to data quality, testing, alert frameworks (e.g., dbt test packages) and building alerting workflows
Experience managing schema drift and changing business processes
Basic Python proficiency for pipeline orchestration or tooling