Role & seniority

Stack / tools

Top 3 responsibilities

Design, implement, and maintain end-to-end data quality testing across raw→bronze→silver→gold layers (dbt tests, unit tests, end-to-end tests)
Collaborate with Data Engineers, Analytics Engineers, and business stakeholders to define testing scope, analyze requirements, and ensure data quality aligns with business value
Lead quality initiatives: investigate failed tests, perform root-cause analysis, review PRs, mentor teammates, and drive process automation and continuous improvement

Must-have skills

Nice-to-haves

Location & work type

Note: Emphasis on data quality across modern data platforms, collaboration with business and engineering teams, and leader

Full Description

Years of Experience: 7+ years

Job Mode: Full-time

Work Mode: Remote within Mexico

We are seeking a highly experienced Senior Quality Engineer (19742) with a strong background in data quality, analytics testing, and data engineering validation within the insurance industry. This role focuses on ensuring high-quality data across modern data platforms by defining, implementing, and maintaining robust testing strategies across the full data lifecycle—from raw ingestion to curated analytical layers. The ideal candidate demonstrates technical leadership, continuous improvement, and strong collaboration with engineering and business stakeholders.

Develop and maintain end-to-end dbt testing frameworks, including mocked and expected datasets across bronze, silver, and gold layers.
Define, implement, and maintain dbt models, unit tests, end-to-end tests, and custom test cases.
Analyze engineering and analytics requirements to determine appropriate testing scope and strategies.
Collaborate closely with business stakeholders, Data Engineers (DEs), and Analytics Engineers (AEs) to define testing needs and ensure data quality alignment.
Gain a deep understanding of the Hiscox repositories, tools, and workflows, including VSCode, pytest, dbt, Databricks, Git, Docker, and CI/CD processes.
Investigate failed tests, perform root cause analysis, and support defect resolution.
Review pull requests to ensure adherence to testing standards and data quality best practices.
Actively participate in Agile ceremonies, providing quality insights and recommendations.
Identify opportunities for process optimization, automation, and efficiency improvements while aligning quality efforts to business value.
Demonstrate leadership by mentoring team members and promoting best practices in Quality Engineering.

7+ years of experience in Quality Engineering, with a strong focus on data-centric testing.
Proven experience working within the insurance industry (required).
Strong understanding of data warehousing concepts, ETL processes, and BI platforms.
Solid knowledge of testing strategies, the Test Pyramid, and version control systems.
Proficiency in SQL and scripting languages such as Python.
Excellent communication, collaboration, and stakeholder management skills.
Demonstrated ability to drive continuous improvement across technology, processes, and people.