We are looking for a Data Quality Engineer with strong expertise in data validation,

quality controls, and automation to ensure the accuracy, completeness, consistency,

and reliability of enterprise data.

The adecuado candidate will have hands-on experience developing automated data quality frameworks using SQL, Python, and Databricks, along with a solid understanding of data engineering and governance principles.

Exposure to Data Access Management, access governance, and IAM processes is a plus.

The primary focus of this role is Data Quality Engineering, with opportunities to support access management automation and governance initiatives.

Key Responsibilities

- Design, implement, and maintain data quality rules, validation frameworks,

controls, thresholds, and KPIs.
- Develop complex SQL queries for data profiling, reconciliation, source-to-target validation, and exception identification.
- Build scalable and reusable data quality automation solutions using Python, SQL,

and PySpark.
- Implement automated checks, exception handling, alerting, reporting, and remediation tracking.
- Work with Databricks, Delta Lake, and data pipelines to implement data quality controls across enterprise data platforms.
- Monitor data quality metrics, identify trends, prioritize issues, and collaborate with stakeholders to drive resolution.
- Support data governance practices, including data ownership, stewardship,

metadata management, and quality standards.
- Collaborate with Data Owners, Data Stewards, Data Engineering, and business teams to define and enforce data quality requirements.• Support data access governance initiatives, including access reviews, entitlement management, provisioning workflows, and audit evidence.
- Where applicable, automate access management processes and integrations using Python, REST APIs, and workflow tools.

Required Qualifications

- Strong experience in Data Quality Engineering, Data Validation, or Data Quality





Management.
- Advanced SQL skills, including complex joins, CTEs, window functions,

aggregations, and large-scale data validation.
- Strong Python programming skills for data validation, automation, exception handling, and reusable framework development.
- Experience building automated data quality checks, monitoring solutions, and validation frameworks.
- Hands-on experience with Databricks, PySpark, Delta Lake, or similar data processing technologies.
- Understanding of ETL/ELT processes, data pipelines, data transformations, and data integration.
- Knowledge of data quality dimensions, including accuracy, completeness,

consistency, validity, uniqueness, and timeliness.
- Experience defining data quality KPIs, thresholds, controls, and remediation processes.
- Strong analytical, problem-solving, communication, and stakeholder management skills.

Preferred Qualifications

- Experience with enterprise data quality or governance platforms such as

Informatica DQ, Collibra, Atlan, IBM, or equivalent tools.
- Familiarity with AWS-based data platforms and cloud data processing.
- Exposure to Data Access Management, Identity and Access Management (IAM),

access governance, and entitlement management.
- Understanding of RBAC/ABAC, access provisioning, access reviews, and least-

privilege principles.
- Familiarity with Active Directory, SailPoint, Databricks Unity Catalog, or equivalent technologies.• Experience integrating enterprise systems through REST APIs and automating workflows using Jira or similar platforms.
- Exposure to access certification automation, audit trails, and compliance reporting.
- Experience developing lightweight internal tools or dashboards using Streamlit or similar frameworks.

Core Skills

Primary focus: Data Quality Engineering, SQL, Python, Data Quality Automation,

Databricks, and Data Validation.

Additional skills: Data Governance, Data Access Management, IAM, RBAC/ABAC,

REST APIs, and Access Certification Automation.

📌 Data Quality Engineer / Data Access Management (México)
🏢 Coforge
📍 México