Role Summary
As a QA Engineer focused on data, you will work closely with data engineers, analytics teams and stakeholders to ensure the accuracy, reliability, and integrity of our data pipelines, transformation workflows and downstream data products. Your mission: validate that data moving through ingestion, cleansing, transformation, and loading meets business rules and quality standards.
Responsibilities
- Design, develop and execute test plans, test cases and automated validation scripts for ETL/ELT/data-pipeline workflows (batch &/or streaming).
- Validate data ingestion from source systems (structured/unstructured), ensure correct transformation logic, and that data lands in target systems (data lake, data warehouse, analytics platform) as expected.
- Develop and maintain data quality checks, data profiling, data cleansing/validation frameworks.
- Monitor data pipelines for issues such as data drift, schema changes, missing values, duplicates, out-of-range values, inconsistencies.
- Work with data engineers/architects to define and embed data quality rules and metrics (completeness, accuracy, consistency, timeliness).
- Automate data verification tests (for example, as part of CI/CD or data-ops pipelines) to catch defects early in the data lifecycle.
- Document data issues, track defects, and collaborate with engineering teams to root-cause, resolve and prevent data quality problems.
- Review data transformations, business logic, data flows and help design test strategy for downstream reporting/analytics/data-science use cases.
- Provide visibility into data quality via dashboards, metrics, alerts and stakeholder reporting.
- Advocate for data-quality best practices across the organization (data quality-by-design, observability, proactive monitoring).
Key Skills & Qualifications
- Strong SQL skills (writing complex queries for verifying data, profiling, checking integrity).
- Experience with one or more programming languages (e.g., Python, Scala) for automation of data-validation scripts.
- Understanding of data pipeline architectures (Ingestion → Transformation → Loading) and associated tools (ETL/ELT, streaming, batch).
- Familiarity with data warehouse / data lake / Big Data technologies, cloud environments (AWS, Azure, GCP) is a plus.
- Experience designing test cases for data pipelines (e.g., verifying schema, row counts, aggregations, joins, deduplication, null handling).
- Ability to identify and monitor data-quality metrics (accuracy, completeness, consistency, timeliness), implement monitoring/alerting.
- Excellent analytical skills: able to break down complex data flows, detect anomalies, drill into root causes.
- Good communication skills: to liaise with business stakeholders, data engineers, analysts and clearly document issues and findings.
- Prior experience in QA/testing (software or data) and familiarity with test-automation frameworks is helpful.
- Preferably a degree in Computer Science, Data Science, or related field (or equivalent experience).
- Language: English (business level)
Preferred / Nice-to-Have
- Experience with streaming data technologies (Kafka, Kinesis) or real-time pipelines.
- Knowledge of modern data-observability tools (monitoring data quality, schema drift, data-drift detection).
- Experience in data-governance, metadata management, or data-cataloging.
- Familiarity with DevOps/CI-CD pipelines (embedding data-tests into production builds).
- Domain experience (finance, healthcare, ecommerce) where data correctness and regulatory compliance matter.
- Language: Japanese
What We Offer
- Opportunity to impact the foundation of how the organization uses data and decision-making.
- Work in a cross-functional, data-driven culture with engineers, analysts & business partners.
- Exposure to modern data platforms and technologies.
- International Global Environment.
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