Genius AI Senior Analytics Engineer to own Genius AI's dbt project architecture and data modeling standards. Collaborate across teams to drive data-driven insights.
Responsibilities
As a Senior Analytics Engineer, you'll own how Genius AI models, defines, and trusts its data. You sit at the intersection of data and the business, curating data so that it can be used to drive insights, strategy, and decision making. Our agentic products are scaling and our architecture is moving toward a more software-oriented model, which means the dbt project you design now will set the standard for how we understand customer behavior and how our AI-powered products make decisions. You won't inherit a finished playbook. You'll write one, and you'll ship changes in days, not quarters, with the judgment to know when to slow down. We expect you to challenge how we work, not just execute against it.
Own the architecture of our dbt project, including design patterns, modeling standards, and the best practices to design efficient, effective data models that will be leveraged across the business to drive insight and action
Partner with PDE (product, design, engineering) teams to support new features and architectural changes by reviewing design docs, creating or updating dbt models as data sources evolve, and validating that metrics, reports, and downstream consumers stay accurate
Work with business partners in Marketing, RevOps, Sales, and Finance to scope their data requests, gather requirements, and design and build data solutions (rETLs, data models, metrics, reporting) to curate data meaningfully
Build, refactor, and optimize dbt models that turn raw product and customer data into accurate, scalable datasets, connecting ideas across all of our data models
Build, refactor, and optimize dbt models that turn raw product and customer data into accurate, scalable datasets and correcting fast when the data says we're wrong
Use AI to build, not just work: design automated testing, documentation, and data workflows that scale beyond your own capacity and free your judgment for the problems that need it
Collaborate with data engineering to shape ingestion and transformation pipelines across Snowflake, dbt, Segment, Matia, Fivetran, and Airflow as we move toward a service-oriented architecture
Drive data privacy, governance, and quality standards that protect the integrity of the data our customers trust us with
Qualification
Default to actionAdvanced SQL proficiencyHands-on experience with data modelingFamiliarity with our stack (Snowflake
Required
5+ years of experience as an analytics engineer, data engineer, or reporting engineer, with 2+ years of hands-on dbt development
You've built AI-assisted workflows for modeling, testing, or documentation rather than only using AI-assisted editors
Default to action: you set a direction, ship something, and iterate on what you learn instead of waiting weeks for perfect alignment
Advanced SQL proficiency, with a track record of writing models that stay fast and readable as data volume grows
Hands-on experience with data modeling, ETL/ELT development principles, and data warehousing concepts
