Sr. Analytics Engineer

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

• 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.