Senior Data Analyst, Business Operations

Build and own a bidirectional HubSpot ↔ BigQuery integration so the CRM and the warehouse agree.

Build pipelines from the rest of the stack: Gong, LinkedIn, GCP and Azure billing, and engineering activity. Use Airbyte where it works and write custom pipelines against REST APIs and direct database connections where it doesn't. Model the results in dbt.

Operate them: monitoring, alerting, backfills, deduplication, and schema drift. Jobs run on GitHub Actions against incremental dbt models.

Define how sales and marketing data is structured: accounts and contacts, segmentation, attribution, activity, and product usage, down to field definitions and how each is captured. This includes the split between our two buying motions, professor-led adoption and instructional-designer-led adoption.

Enforce it with automated checks that run on a schedule, and hold the standard when someone asks for an exception.

Reconcile before you publish. A silently wrong number is worse than a missing one.

Work with sales, marketing, and customer success to turn requests into defined questions, and push back on requests that aren't worth building.

Land recurring answers in our internal dashboards so teams pull their own numbers.

Build customer health scoring that customer success works from, early enough to act on.

Model sales forecasts and customer segments once the underlying data supports it.

Track cloud, inference, and vendor spend per customer so we know margin per customer.

BigQuery or a comparable cloud warehouse (Snowflake, Redshift, Databricks).

dbt or a comparable transformation framework (SQLMesh, Dataform).

Airbyte or comparable integration tooling (Fivetran, Meltano).

Airflow, Dagster, or comparable orchestration.

HubSpot or Salesforce data modeling.

Forecasting, segmentation, clustering, or churn modeling.

Cloud cost analysis.

Edtech, higher education, or teaching background.

Interest in growing toward machine learning, statistics, and advanced analysis. Or, existing ML and DS experience.

A systems-thinking approach: you care how the pieces fit together and build things meant to last.

No degree requirements. What you know and can do matters more than where you learned it.

90 days: HubSpot ↔ BigQuery syncing both directions in production. Sales and marketing data model documented with field-level definitions.

6 months: Gong pipeline live. Automated data quality checks running on a schedule. Sales and marketing dashboards in production. Customer health scoring live.

9 months: Margin per customer visible.

We're a startup, which means the work moves fast, but the impact is real. You will not be a small cog in a big machine here, but the one building the machine itself. This is a full-time, remote position, open to candidates in the United States, with meaningful overlap with US business hours.

You'll be one of the few people who can see the whole business at once. We want someone who finds that interesting rather than daunting, and who'll tell us what the data says even when it's inconvenient.