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 idóneo 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
- 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

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 (México)
🏢 Coforge
📍 México