What are the responsibilities and job description for the Product - Data Analyst position at Basis?
About Basis
Basis equips accountants with a team of AI agents to take on real workflows.
We have hit product-market fit, have more demand than we can meet, and just raised $34m to scale at a speed that meets this moment.
Built in New York City. Read more about Basis here.
What You’ll Be Doing
As a Product - Data Analyst on our Product Platform team, you’ll help build the data foundation that powers our AI accounting platform. You’ll act as the connective tissue between engineering, ML research, and product—owning data quality, defining guardrails, and ensuring the datasets fueling our intelligence layer are precise, explainable, and production-ready.
This role blends deep analytical skill with an understanding of how financial data behaves in the real world. You’ll help shape how data flows through our platform—from ERP integrations to machine learning workflows—so that our financial data is trustworthy, explainable, and ready for automation and analytics.
You’ll work side by side with ML researchers, data engineers, and accountants to turn raw data into high-fidelity assets that make AI accounting trustworthy, auditable, and transformative.
You Will
What You’ll Bring
Basis equips accountants with a team of AI agents to take on real workflows.
We have hit product-market fit, have more demand than we can meet, and just raised $34m to scale at a speed that meets this moment.
Built in New York City. Read more about Basis here.
What You’ll Be Doing
As a Product - Data Analyst on our Product Platform team, you’ll help build the data foundation that powers our AI accounting platform. You’ll act as the connective tissue between engineering, ML research, and product—owning data quality, defining guardrails, and ensuring the datasets fueling our intelligence layer are precise, explainable, and production-ready.
This role blends deep analytical skill with an understanding of how financial data behaves in the real world. You’ll help shape how data flows through our platform—from ERP integrations to machine learning workflows—so that our financial data is trustworthy, explainable, and ready for automation and analytics.
You’ll work side by side with ML researchers, data engineers, and accountants to turn raw data into high-fidelity assets that make AI accounting trustworthy, auditable, and transformative.
You Will
- Be the source of truth: Own the accuracy and trustworthiness of financial data flowing through our platform—every number, reconciliation, and migration matters.
- Fuel the AI: Ensure the data that powers our models and automation is clean, labeled, and ready—so insights are explainable and intelligent features truly shine.
- Delight users through data: Turn data accuracy into user confidence—when accountants and analysts trust what they see, they love the product more.
- Keep the pipes healthy: Monitor, validate, and debug integrations across ERPs, pipelines, and internal systems to maintain freshness, completeness, and consistency.
- Catch the invisible: Detect subtle data drifts or mapping errors before they surface to users—then trace and resolve them with precision.
- Build guardrails for scale: Define validation suites, automated checks, and release gates that keep data quality high as we ship new features and integrations.
- Discover and improve: Explore patterns in accounting data to surface insights, edge cases, and opportunities for better automation and product intelligence.
- Continuous Improvement: Build and evolve our internal data documentation, runbooks, and validation playbooks to accelerate onboarding and increase confidence in data decisions.
What You’ll Bring
- 3–6 years working in data analytics, integration, or quality roles within enterprise software, fintech, or accounting systems, with analytical mindset and strong data skills.
- Systems thinker: Enjoys connecting dots between application features, backend data, and user experience; comfortable diagnosing both data-level and logic-level issues.
- Quality-minded: understanding of accounting and finance workflows with hands-on experience testing both data and application behavior to ensure correctness and reliability.
- AI-curious: Fascinated by how structured financial data can power machine reasoning, anomaly detection, and generative workflows.
- Builder mindset: Treats processes as products—automating manual steps, codifying checks, and driving continuous improvement.
- Advanced SQL skills for deep data validation and debugging complex data pipelines.
- Programming experience (e.g. Python) to design and implement custom data validation and transformation logic.
- Comfort working with AI/ML datasets and helping define labeling, validation, or explainability standards.
- Familiarity with ERP systems (e.g., NetSuite, QuickBooks, or Xero) and their data structures.
- Experience with ETL pipelines, API-based integrations, or data observability tools.
- Background in accounting data reconciliation, audit workflows, or financial data modeling.
- Data refreshes are faster, cleaner, and consistently accurate, enabling near-real-time insights for users.
- You’ve built automated validation and reconciliation systems that catch issues before they reach production.
- Financial datasets across integrations are trustworthy and explainable, supporting both automation and ML model performance.
- Teams (ML, product, deployed intelligence) rely on your systems and documentation to make faster, more confident decisions.
