Cloud Data Integrity Expert

4 weeks ago

London, Greater London, United Kingdom Triple Point Group Full time
Job Requirements

We are seeking a skilled Cloud Data Integrity Expert to join the Triple Point Group team. The successful candidate will play a critical role in ensuring the accuracy, consistency, and reliability of our data across all systems.

About the Role

  • This is a full-time position with an estimated salary of $160,000 - $200,000 per annum, depending on experience.

Main Responsibilities

Data Quality Assurance and Automation
  • Perform rigorous data validation and testing to support data migration efforts, ensuring high standards of data quality and integrity.
  • Collaborate with data engineering on ELT testing, specifically in identifying and troubleshooting errors in data movement and transformation processes.
  • Establish and implement automated data validation frameworks that ensure data accuracy, consistency, and reliability across our data pipeline.
  • Create and maintain CI/CD-integrated QA workflows to support real-time testing and automated quality checks, focusing on Azure and Snowflake infrastructure.
  • Develop automated test cases for data validation, focusing on critical workflows, data transformations, and API integrations within both Snowflake and Azure.
Collaboration & Support for Data Engineering
  • Work closely with the Senior Data Engineer to set and maintain quality standards for the Snowflake data warehouse, supporting scalability and reporting needs.
  • Participate in data architecture discussions, providing QA insights on best practices for data collection, transformation, and loading processes to maintain end-to-end quality.
  • Ensure data reliability and accuracy by proactively identifying potential data discrepancies and issues, particularly API data integration, CI/CD workflows.
  • Develop reusable, modularised test scripts in Python and SQL to streamline testing across various stages of the data pipeline.
  • Conduct regular data cleansing activities, identifying, documenting, and addressing data anomalies within the financial lending dataset.
  • Partner with business stakeholders to understand data needs and ensure that data integrity meets end-user requirements for accurate reporting and analysis.
  • Support the development of data governance and QA processes to streamline issue resolution and establish best practices for ongoing quality control.
Documentation & Process Improvement
  • Document QA processes, test cases, and key findings, and quality control standards to establish a knowledge base for quality assurance.
  • Proactively contribute to the development of data governance standards and practices, including version control and GitHub workflows to support efficient change management.

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