Software Engineer, Data Quality

About the Role

Customers commit real money based on the numbers Archera shows them, so a wrong number is not a broken dashboard — it is a wrong financial decision. Getting it right is genuinely hard: our product spans a large surface area, and AWS, Azure, and Google Cloud each represent billing in a different format. As a Software Engineer focused on data quality, you will build the automated validation, reconciliation, and anomaly detection that keeps problems from ever reaching a customer, and you will become one of the few engineers anywhere who knows how all three providers really model their billing data.

Responsibilities

• Go deep on the product's data model and on how AWS, Azure, and Google Cloud each format billing differently, documenting what you find rigorously.
• Build automated data validation, reconciliation, and anomaly detection, including the workflows (Argo or equivalent) that run the checks.
• Investigate data-quality problems to root cause, fix them, and automate the check that prevents a repeat.
• Replace the manual data checks the team runs before customer demos with durable, automated coverage.
• Extend test automation to critical product surfaces.
• Work with sales and customer success on live data questions, diagnosing and resolving issues as they surface.
• Use AI tooling to accelerate investigation, check generation, and anomaly triage.

Qualifications

• 3+ years of software or data engineering experience, or equivalent hands-on experience building and shipping code.
• Strong attention to detail — you notice the number that is subtly wrong, and you care because someone is budgeting against it.
• SQL fluency for transformation and reconciliation, plus comfort scripting. Python is preferred; a strong engineer who knows SQL and another language and will pick up Python works well here.
• A preference for automating a check rather than repeating it.
• Interest in becoming the definitive expert on how three cloud providers each represent billing — knowledge very few engineers have.
• Comfort with AI tooling, used with judgment, to raise coverage and move faster.

Nice-to-Haves

• Cloud billing across AWS, Azure, or Google Cloud, FinOps, or other financial-data domains.
• Data-quality frameworks such as dbt tests, Great Expectations, or Soda.
• Workflow orchestration (Argo, Airflow, Dagster) and data pipeline or ETL experience.
• Test automation and CI for data or application surfaces.
• Experience making data trustworthy for customer-facing or revenue-critical use.

Benefits

• Competitive salary and equity package
• Comprehensive medical, dental, and vision coverage
• 401(k) plan
• Flexible PTO and company holidays
• Remote-first culture
• Opportunity to shape and scale a category-defining FinTech platform