Senior Data Quality Engineering Analyst

Jobtailor; Irvine (CA); On-site; USD 120,000 - 150,000; Full time

Job summary

Jobtailor is seeking a senior QA Data Quality engineer to lead validation across data pipelines and ensure data integrity.

You will design automated test frameworks, collaborate with data engineers, and implement quality checks in batch and streaming jobs using tools such as dbt and Great Expectations.

Qualifications

• Bachelor of Science in Information Technology degree.
• 7+ years hands-on Quality Assurance experience.
• 5+ years with AWS, JSON, XML, and Snowflake.
• 3+ years of automated test scripting for validation.
• Experience validating data quality in key domains.
• Strong partnership with data engineers to embed quality checks.
• Experience with automated testing frameworks for data pipelines.
• Knowledge of dbt and Great Expectations.
• Willingness to train junior resources and learn new tech.

Responsibilities

• Understand complex business processes and attend daily stand-up meetings.
• Drive validation needs by creating test plans, scenarios, and environments.
• Coordinate with onsite and offshore teams to meet project timelines.
• Identify and socialize project risks for informed decisions.
• Design and implement a data quality framework for batch and real-time pipelines.
• Embed quality controls across the data lifecycle and pipelines.
• Develop automated test scripts to support validation.
• Track QA metrics like defect leakage and densities.

Skills

Test planning

Automation testing

Data quality validation

CI/CD

Data pipelines

Mentoring juniors

Curiosity to learn

Education

Bachelor of Science in Information Technology

Tools

AWS

Snowflake

Dbt

Great Expectations

Job description

Responsibilities

• Able to understand complex business process, technology landscape, related requirements and attend daily stand‑up meetings to focus on key focus areas of validation.
• Able to independently drive validation needs by creating comprehensive and well‑structured test plan, test scenario, test case, setting up test environment, working with business analyst and developer to understand requirements and architecture, reviewing test results of junior team members and guiding them with validation approach.
• Able to coordinate with key onsite and offshore counterparts to complete validation and certification activities within agreed upon project timeline.
• Able to identify, call out and socialize project risks so stakeholders can take informed decisions.
• Designing and implementing a comprehensive data quality framework involving building of automated validation framework across both batch and real‑time pipelines, enforcing core quality dimensions – completeness, accuracy, consistency, and timeliness – through checks like schema validation, data drift and anomaly detection, referential integrity and business rule enforcement, while defining and tracking KPIs and SLAs to continuously monitor and uphold data health across the organization.
• Embedding data quality controls within the pipelines involves integrating validation across the full data lifecycle, from ingestion through transformation to consumption, supporting both batch and streaming workloads.
• Able to bring in hands‑on development experience in test scripts to automate test process. Should deliver applicable automated test solution to provide appropriate solution within a stipulated timeframe.
• Track quality assurance metrics such as defect leakage, defect densities and open defect counts.

Requirements

• Have a Bachelor of Science in Information Technology degree.
• Have at least 7 years’ thorough hands‑on experience in Quality Assurance Process.
• Have at least 5 years of experience with AWS services, JSON and XML files, Snowflake database, etc.
• Have at least 3 years of test script development experience for automated validation of applications under test.
• Highly preferred experience validating data quality within key domains such as Restaurant Front of House, Back of House, Customer, etc.
• Maintain strong partnership with data engineers to embed quality checks directly into pipelines.
• Knowledge of developing and maintaining automated testing frameworks for data pipelines and integrating with CI/CD processes for continuous validation.
• Knowledge of enabling reusable validation patterns using tools like dbt, Great Expectations, or custom frameworks.
• Passion to train and groom junior resources in the team and expand technical knowledge throughout the organization.
• Passion to learn new technology and processes, curiosity to collaborate with subject matter experts.

Core Competencies

Demonstrates expertise in Quality Assurance processes, including the development of automated testing frameworks and data quality validation across various domains. Proficient in collaborating with cross‑functional teams to ensure data integrity and quality throughout the data lifecycle.

Tools & Technologies

• AWS
• Snowflake
• Dbt
• Great Expectations
• CI/CD Processes