Data Integrity Lead

About This Role

As a Data Integrity Lead, you will take ownership of data quality, metric consistency, and reporting reliability across the organization. This role focuses on strengthening Jiffy's semantic layer in dbt, improving metric governance, and building foundational data quality and monitoring systems to ensure accurate and timely metrics for business decisions.

Responsibilities

• Own metric definitions and governance across the business, ensuring key metrics are consistently calculated and trusted across reporting.
• Build and maintain a strong semantic foundation in dbt, including well-structured fact and dimension models within Redshift.
• Develop and improve automated validation workflows to detect missing data, anomalies, metric drift, and freshness issues before stakeholders are impacted.
• Investigate and surface data reliability issues such as delayed replication, shipment-related data gaps, and pipeline refresh failures, partnering with engineering teams to resolve root causes.
• Create and maintain a clear "metric bible" documenting how core KPIs (e.g., Gross Profit) are sourced, calculated, and used.
• Implement foundational data integrity systems such as quality scorecards, metric registries, and CI/CD checks within dbt workflows.
• Enable self-service analytics across teams by supporting reliable models and tools (e.g., Hex) that reduce dependency on ad-hoc reporting.

Requirements

• 6+ years of experience in data engineering, analytics engineering, or data reliability-focused roles.
• Strong proficiency in SQL
• Experience navigating complex data warehouse environments
• Hands-on experience with Amazon Redshift and dbt
• Strong understanding of how warehouse tables connect to operational source systems (e.g., Postgres)
• Ability to write and maintain code for data validation, automation, and monitoring
• Strong documentation and organizational skills

Qualifications

• Bachelor's degree in Computer Science, Data Analytics, Engineering, or a related field (or equivalent practical experience).
• 6+ years of experience in data engineering, analytics engineering, or data reliability-focused roles.

Nice to Have

• Experience building or scaling data quality frameworks (e.g., anomaly detection, freshness monitoring)
• Familiarity implementing CI/CD best practices for analytics engineering and dbt testing workflows
• Experience creating semantic layers, certified marts, or metric definition registries
• Background supporting ecommerce or operational analytics
• Familiarity with self-service analytics tools such as Hex

Skills

* Required skills

Benefits