Data QA engineer (manual)

ТРТ; United States; Remote; USD 90,000 - 120,000; Full time

Benefits offered by this job

Unlimited PTO

Company hardware after probation

Paid New Year's holidays

Job summary

ТРТ is seeking a mid-level Data QA Engineer to validate ETL data loads and ensure BigQuery and Spanner datasets are accurate, complete, and aligned with business rules. You will verify source-to-target reconciliation, data transformation logic, and data freshness, while collaborating with data engineers on root-cause analysis.

The role emphasizes data quality, risk reduction, and robust test documentation, with an eye toward PHI handling and CI/CD integration in a dynamic cloud environment.

Qualifications

• Experience in data QA/testing data-intensive systems.
• Strong SQL skills and data validation expertise.
• Ability to perform source-to-target reconciliation and trace data issues.
• Experience with regression, smoke, and exploratory testing.
• Knowledge of Agile methodologies and bug reporting practices.

Responsibilities

• Test ETL/ELT data loads into BigQuery and Spanner from multiple sources.
• Run data quality checks and validate consistency, integrity, and business rules.
• Investigate data discrepancies and trace to source, load, or transformation steps.
• Collaborate with data engineers to perform root-cause analysis and verify fixes.
• Contribute to CI/CD by integrating data tests into pipelines.
• Create and maintain test cases and documentation.
• Avoid exposing PHI in tickets and logs, and review AI-generated outputs.

Skills

SQL

Data QA

Data validation

Agile

Analytical thinking

Communication

Tools

Zephyr

TestRail

Jira

Git

Airflow

dbt

Job description

Описание

Stealth-mode AI-powered Cloud-Native Health-Tech company is looking for a great, long-term Mid-level Data QA Engineer. The focus of the role is testing ETL data loads and making sure the data in BigQuery and Spanner is complete, accurate, and consistent with business requirements. Day to day, you will validate data loads from source to target, check transformation logic, investigate data discrepancies, and document issues so engineers can fix them quickly. Pipeline development and fixes are handled by data engineers; your role is to find problems, trace where they come from, and confirm that fixes work.

Задачи

Test ETL data loads into BigQuery and Spanner from a wide range of sources. Run data quality checks on datasets already stored in BigQuery and Spanner: consistency, integrity, and business rule validation. Perform source-to-target reconciliation: row counts, checksums, aggregate comparison, duplicate and null checks. Validate transformation, mapping, and standardization logic against business requirements. Check incremental vs. full loads, re-runs, and late-arriving data. Run data freshness and completeness checks. Detect schema and file format changes and assess their impact on downstream data. Investigate data discrepancies and trace them to the source, load, or transformation step where they originate. Report issues with clear evidence: queries, sample records, expected vs. actual results. Work with data engineers on root-cause analysis; verify fixes and confirm the data is correct before closing issues. Use AI tools to write SQL checks, prepare test cases, and analyze issues faster. Critically review AI-generated output and always verify results, especially when working with PHI. Create and maintain test cases and test documentation. Participate in CI/CD processes by integrating data tests into pipelines. Track recurring data issues and raise them so they get a permanent fix or a new automated check. Help expand test coverage as new sources and transformations are added. Learn and apply medical domain knowledge (clinical codes, terminology, business rules) to validate healthcare-specific data.

Требования

Mid-level experience in data QA or testing of data-intensive systems. Solid understanding of data validation strategies, data profiling, and data integrity checks. Experience with regression, smoke, and exploratory testing. Experience testing ETL/ELT data loads and validating data flows across multiple sources and sinks. Strong SQL skills and a solid grasp of relational database concepts. Experience with test management tools (Zephyr, TestRail, Jira) and Git. Hands-on experience using AI tools in daily QA work. Willingness and ability to learn a large amount of healthcare domain knowledge. Strong analytical skills and attention to detail. Ability to turn business requirements into concrete data checks. Clear written communication: bug reports and data quality concerns that engineers and analysts can act on. Experience working in Agile teams. Ability to work independently and grow into more complex testing and analytical tasks. Willingness to explore new tools and approaches for data testing. Because the system handles patient data (PHI), care with sensitive data — never copying PHI into tickets, logs, or chat — is required. Ability to work in an iterative development workflow, improving data quality and test coverage step by step rather than through a Waterfall-style approach. Experience collaborating with data engineers, analysts, and product managers to turn data discrepancies into clear reports, root-cause findings, and follow-up work.

Будет плюсом

Experience with BigQuery and the GCP ecosystem; Experience with Cloud Spanner or other distributed databases; Experience with Airflow and dbt; Familiarity with healthcare coding systems and terminologies: ICD-10, CPT, HCPCS, LOINC, SNOMED CT, RxNorm, NDC; Cloud-native familiarity is preferred. Candidates comfortable in Google Cloud Platform (GCP) will be prioritized, although experience with other major cloud providers (e.g., AWS or Azure) is also valuable; Experience in Healthcare, Health-Tech, and MedTech is an advantage; Other experience advantages a candidate may have include working with healthcare data (claims, eligibility, lab results, risk adjustment, quality measures), experience with payor or CMS data files, and basic scripting to automate repetitive checks.

Условия

The company has Unlimited PTOs Policy and compensated New Years Holidays on top of that. The misuse of the policy isn’t welcomed, though it’s definitely possible to take at least two weeks – and fully compensated – vacation, or more. The company provides Corporate Hardware for employees who completed their Probationary Period, as well as proven their value.