Data QA Engineer

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.

Since working with healthcare data, an important part of the job is learning the medical domain: clinical codes, terminology, and business rules that define what correct data looks like.

The company uses AI tools actively in our work and expects you to do the same, for example, to write SQL checks, prepare test cases, and analyze issues faster, while always verifying the results yourself.

This role is a good fit for someone with strong SQL skills, experience testing ETL processes, attention to detail, and an interest in learning a complex business domain.

Compensation and Benefits:

Paid Time Off

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.

Corporate Hardware

The company provides Corporate Hardware for employees who completed their Probationary Period, as well as proven their value.

Target Stack

NOTE: Similar cloud-native experience is always an option.

NOTE: Hands-on experience with every tool is NOT required; strong SQL and data testing skills are what matter most.

Some Bits of Our Stack

• SQL (complex queries, window functions, CTEs)

• AI tools (Claude, ChatGPT, GitHub Copilot, Cursor)

• Test management tools (Zephyr, TestRail, Jira)

• Git (GitHub)

• BigQuery / Cloud Spanner

• Cloud Storage

• Airflow

• dbt

• Slack

Responsibilities:

ETL Data Load Testing

• 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.

Issue Investigation & Reporting

• 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.

AI-Assisted Testing

• 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.

Documentation & Continuous Improvement

• 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.

Healthcare Domain

• Learn and apply medical domain knowledge (clinical codes, terminology, business rules) to validate healthcare-specific data.

Required Experience

• 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.

Nice to Have

• 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.

General Skills

• 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.

Cloud-Native & GCP

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.

Business Domain

Experience in Healthcare, Health-Tech, and MedTech is an advantage. Because the system handles patient data (PHI), care with sensitive data — never copying PHI into tickets, logs, or chat — is required.

Fundamentals:

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.

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.

Команда экспертов, предоставляющая аналитические услуги для клиентов в сфере здравоохранения, ищет Data QA.

Вы станете частью международной команды высококлассных профессионалов, объединённых стремлением создавать продукты, которые реально улучшают качество медицинских услуг.

Обязанности:

Тестирование загрузки данных ETL

• Проводить тестирование загрузки данных ETL в BigQuery и Spanner из широкого спектра источников.

• Выполнять проверки качества данных в наборах данных, уже хранящихся в BigQuery и Spanner: проверку согласованности, целостности и соответствия бизнес-правилам.

• Выполнять сверку данных между источником и целевой системой: подсчет количества строк, контрольные суммы, сравнение агрегатов, проверку на наличие дубликатов и нулевых значений.

• Проверять логику преобразования, сопоставления и стандартизации на соответствие бизнес-требованиям.

• Проверять инкрементные и полные загрузки, повторные запуски и запоздавшие данные.

Расследование проблем и составление отчетов

• Расследовать несоответствия в данных и выявляьб их источник — этап загрузки или преобразования, на котором они возникли.

• Составлять отчеты о проблемах с приведением четких доказательств: запросы, образцы записей, сравнение ожидаемых и фактических результатов.

• Совместно с инженерами по данным проводить анализ первопричин; проверять исправления и убеждаться в правильности данных перед закрытием задач.