Software Engineer, Data Quality

Archera•New York, NY

1d•Remote

About The Position

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.

Requirements

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.

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.