Senior Data Analyst, Business Operations

Senior Data Analyst, Business Operations

Remote (United States)

Location: United States

Workplace: Remote

Employment Type: Full Time

Core Areas: Business Operations Analytics, Data Pipelines, BigQuery, dbt, Python, Data Quality, Sales & Marketing Analytics, Forecasting

Compensation: $139,000-$167,000 per year plus equity

About the Role

This opportunity is for a Senior Data Analyst, Business Operations who will own internal-facing data across sales, marketing, customer success, finance, operations, and engineering. The role spans data pipelines, data modeling, data quality, dashboards, and business analysis, with an initial six-month priority on sales and marketing data. The position will independently prioritize and deliver business-side data initiatives while partnering directly with non-technical stakeholders.

The existing stack includes BigQuery, dbt, Airbyte, GitHub Actions, Python, and custom internal dashboards. This role will extend that production environment by building integrations, maintaining trustworthy data models, automating quality controls, and developing analytics such as customer health scoring, sales forecasting, segmentation, and customer-level margin analysis.

What You'll Do

• Build and own a bidirectional HubSpot-to-BigQuery integration so CRM and warehouse data remain aligned.
• Build data pipelines from Gong, LinkedIn, GCP and Azure billing, and engineering activity sources.
• Use Airbyte where appropriate and develop custom pipelines using REST APIs and direct database connections when additional flexibility is required.
• Model integrated data in dbt.
• Operate production pipelines through monitoring, alerting, backfills, deduplication, and schema drift management.
• Run jobs through GitHub Actions using incremental dbt models.

• Define the structure of sales and marketing data, including accounts, contacts, segmentation, attribution, activity, and product usage.
• Establish field-level definitions and data capture standards, including distinctions between professor-led adoption and instructional-designer-led adoption.
• Enforce data standards through automated, scheduled quality checks.
• Maintain consistent data standards when exceptions are requested.
• Reconcile data before publishing results to prevent inaccurate metrics from reaching business users.

• Work directly with Sales, Marketing, and Customer Success to translate business requests into clearly defined analytical questions.
• Evaluate incoming requests and push back when proposed analytical work does not justify the effort required.
• Build recurring answers into internal dashboards so business teams can independently access their metrics.
• Develop customer health scoring that gives Customer Success teams actionable signals early enough to respond.
• Build sales forecasting and customer segmentation models once the underlying data is sufficiently reliable.
• Track cloud, inference, and vendor spend by customer to measure customer-level margin.

Qualifications

Required Experience

• Hands-on experience building, deploying, and operating production data pipelines from REST APIs, databases, and third-party SaaS platforms.
• Experience working directly with non-technical stakeholders to scope analytical questions and deliver analysis.
• Experience independently prioritizing work in an environment where this role serves as the primary business-side data resource.

Required Skills

• Production-level Python skills, including writing code that is version-controlled, tested, scheduled, and deployed.
• Strong engineering fundamentals, including Git, CI/CD, cron, and deploying code to production.
• Daily experience using AI coding tools as part of development workflows.
• Ability to independently manage priorities and make decisions about which data and analytics initiatives to pursue.
• Strong written communication skills suited to a remote, asynchronous working environment.

Preferred Qualifications

• Experience with BigQuery or a comparable cloud data warehouse such as Snowflake, Redshift, or Databricks.
• Experience with dbt or a comparable transformation framework such as SQLMesh or Dataform.
• Experience with Airbyte or comparable data integration tools such as Fivetran or Meltano.
• Experience with Airflow, Dagster, or comparable orchestration tooling.
• Experience modeling HubSpot or Salesforce data.
• Experience with forecasting, segmentation, clustering, or churn modeling.
• Experience analyzing cloud costs.
• Background in edtech, higher education, or teaching.
• Interest in developing deeper expertise in machine learning, statistics, and advanced analytics, or existing machine learning and data science experience.
• A systems-thinking approach to designing reliable, maintainable data solutions.

Education

• No degree is required.

Benefits

• Comprehensive medical, dental, and vision coverage for U.S.-based employees.
• 401(k) with employer match for U.S.-based employees.
• Unlimited paid time off.
• Equity through stock options.
• Flexible, remote-first work environment.

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