USA_Developer

Pay Rate Range

$ 68.18 - 75.75/hr.

Data Assurance Engineer

Senior Data QA / Tester (Snowflake, dbt, Fivetran)

Job Description

We are seeking a Senior Data QA / Tester with strong hands-on experience in validating modern data pipelines and transformation frameworks built on Snowflake and dbt. This role is responsible for ensuring the accuracy, completeness, reliability, and quality of data pipelines and transformations developed by data engineering teams.

The ideal candidate brings deep experience in data validation, SQL-based testing, dbt testing frameworks, and automation, along with strong attention to detail and accountability. Experience with Fivetran-based ingestion validation is a plus, but the primary focus is testing Snowflake and dbt-based transformations.

Key Responsibilities

• Define and implement end-to-end data testing strategies for pipelines built using Snowflake, dbt, and Fivetran.

Validate dbt transformations, including

o Data model validation (staging, intermediate, marts)

o Validation of SCD Type 2 logic (snapshots)

o Testing of macros and transformation logic

o Ensuring correctness of business rules implemented in SQL/dbt

Design and execute data validation test cases, including

o Source-to-target reconciliation

o Data completeness, accuracy, and consistency checks

o Edge case and boundary condition testing

Develop and maintain automated data quality checks, including

o dbt built-in tests (not null, unique, relationships)

o Custom dbt tests and SQL-based validation frameworks

o Data anomaly detection and trend validation

Conduct Snowflake data validation and performance testing

o Query result validation

o Data consistency across environments

o Validation of incremental loads and partitioning behavior

Validate Fivetran ingestion pipelines

o Data freshness and latency checks

o Schema validation and drift handling

o Incremental ingestion validation

• Build and maintain test automation frameworks using Python and SQL for scalable validation.

Integrate testing into CI/CD pipelines

o Automate test execution as part of deployment workflows

o Validate dbt runs (dbt test, dbt build) before production release

• Use Git-based workflows for test code versioning, review, and collaboration.
• Work with orchestration tools such as Airflow / Astronomer to validate pipeline execution and monitor failures.
• Collaborate with data engineers, platform teams, and business stakeholders to ensure data quality standards are consistently met.

Required Qualifications

• 10+ years of experience in data testing / data QA / data engineering validation roles.
• Strong SQL expertise with experience writing complex validation queries.

Hands-on experience testing dbt-based transformations, including

o dbt tests (schema and custom)

o Validation of models, snapshots, and transformations

o Understanding of dbt project structure and workflows

Strong Snowflake experience, including

o Data validation and reconciliation

o Understanding of micro-partitions, incremental loads, and query behavior

o Experience validating large-scale datasets

• Experience building automated data testing frameworks using Python and SQL.
• Hands-on experience with Git workflows (version control, pull requests, reviews).
• Experience integrating data testing into CI/CD pipelines.
• Experience validating production-grade data pipelines with strong SLAs and quality expectations.

Preferred Qualifications

• Experience with Fivetran ingestion validation and troubleshooting.
• Experience with Airflow / Astronomer for pipeline validation and orchestration monitoring.
• Exposure to data observability and data quality tools (e.g., Monte Carlo, Great Expectations, etc.).
• Financial services / banking domain experience is strongly preferred and will be prioritized, though not mandatory.

Key Behavioral Expectations

• Demonstrates strong accountability and accountability for data quality and testing outcomes.
• Highly detail-oriented, with the ability to identify subtle data issues and inconsistencies.
• Self-motivated and proactive, able to operate independently and drive validation efforts.
• A continuous learner who stays current with evolving data engineering and quality practices.
• Strong collaboration skills to work effectively with engineering and business teams.

What Success Looks Like

• Ensures high data quality and reliability across all data pipelines and transformations.
• Builds and maintains automated, reusable testing frameworks integrated into CI/CD pipelines.
• Identifies and prevents data issues before production deployment.
• Establishes strong data validation standards and best practices across the engineering team.
• Operates with accountability and consistently delivers accurate, trusted data outputs.Advanced SQL, Python test automation, dbt testing (schema, custom, unit and integration tests), Snowflake data validation and performance testing, source-to-target reconciliation, Fivetran ingestion validation, Airflow/Astronomer, Git and CI/CD.

Skills

Digital

Google Data Engineering

Experience Required

4-6 years

Skills

Category

Name

Required

Importance

Experience

SkillCategoryTest1_MN

Digital

Google Data Engineering

Yes

1

>7 years