Senior Quality Engineer, Data Platform
💰 $144,000 – $213,000/yr
At a glance
- Who can apply
- United States
- Work arrangement
- Hybrid · San Jose, California
- Employment type
- Full-time
- Seniority
- Senior
- Experience
- 5+ years (as stated by the employer)
- Salary
- $144,000 – $213,000/yr · published by the employer
- ✓ Open to early-career candidates
Checked against the employer's source on October 12, 2026. Details come from the employer's posting; confirm them on the application page.
Job Description
What You’ll Do:
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Design, build, and maintain automated test frameworks across the full data stack — covering data pipelines, ETL/ELT workflows, REST APIs, and backend services that power large-scale financial data processing.
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Validate data integrity, schema correctness, and transformation accuracy across ERP-to-FloQast ingestion flows (NetSuite, Sage Intacct, SAP, and others).
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Identify performance bottlenecks and reliability gaps in data-heavy workflows — whether that’s slow ingestion on large customer datasets, pipelines that produce inconsistent results under load, or sync jobs that behave differently across customer environments.
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Build and maintain data quality validation test suites — including row-count checks, referential integrity, financial aggregation accuracy, and cross-system reconciliation.
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Integrate automated tests into CI/CD pipelines with clear test reporting and alerting.
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Contribute to test data management strategies, including synthetic financial data generation and environment seeding for data platform services.
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Collaborate with release teams on release validation, triage defects, and drive root cause analysis on data discrepancies.
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Mentor junior QE engineers and champion a data-quality-first testing culture across the team.
What You'll Bring:
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Bachelor’s or Master’s degree in Computer Science or a related field.
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5+ years of QA automation experience, with at least 2+ years testing data pipelines, data warehouses, or data-intensive backend services.
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Strong proficiency in JavaScript/TypeScript and Python, and test frameworks (Jest, Mocha, Playwright, Cypress).
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Hands-on experience with SQL and data validation — writing complex queries to verify transformation logic, aggregations, and financial data accuracy.
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Experience testing against Snowflake, Redshift, or similar cloud data warehouses.
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Solid understanding of ETL/ELT concepts — data ingestion, transformation, schema evolution, and lineage.
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Experience with REST API testing and backend service validation.
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Proficiency with GitHub Actions and version control (Git).
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Strong debugging skills for data discrepancies — able to trace an issue from UI through API to pipeline and raw data store.
