Data Engineer Specialist (Data Quality)

Job description

**This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Engineer Specialist (Data Quality) based in Brazil.** This role offers the opportunity to serve as a technical reference for Data Quality across large-scale analytical and operational data environments. You will define standards, frameworks, processes, and best practices that strengthen data reliability throughout the full lifecycle of data products. The position combines hands-on engineering with technical leadership, automation, observability, and governance. You will partner closely with Data Engineering, Data Governance, and business teams to establish measurable quality standards and drive continuous improvement. Your expertise will also contribute to Data Products, Enterprise Data Domains, catalogs, lineage, metadata, and certification practices. The role provides broad influence over technical roadmaps while helping raise organizational maturity in Data Quality. You will also mentor engineers and analysts and collaborate with local and global teams in an international environment. ### Accountabilities: - Act as the primary technical reference for Data Quality, defining standards, frameworks, processes, and best practices for analytical and operational data environments. - Design, implement, and continuously evolve automated data quality controls across the complete lifecycle of data products. - Lead Data Quality initiatives in partnership with Data Engineering, Data Governance, and business teams, aligning technical solutions with organizational needs. - Define and implement data quality metrics, validation rules, SLAs, scorecards, and monitoring mechanisms to measure and improve reliability. - Integrate Data Quality practices with DataOps, data observability, CI/CD, testing automation, and broader data governance capabilities. - Support the implementation and evolution of Data Catalog, Data Lineage, Metadata Management, and data product certification processes. - Investigate data quality issues and lead root-cause analysis, coordinating corrective and preventive actions with the teams responsible for affected data products. - Support the definition and adoption of Data Product and Enterprise Data Domain standards, promoting consistency, reliability, and trust across the data ecosystem. - Mentor engineers and analysts by sharing technical knowledge, promoting best practices, and contributing to the organization’s Data Quality maturity. - Influence technical roadmaps and architectural decisions related to data quality, reliability, observability, and governance while facilitating discussions with local and global stakeholders. ### Requirements: - Bachelor’s degree in Technology, Engineering, Mathematics, Statistics, or a related field. - Solid Data Engineering experience in large-scale corporate analytical environments, with demonstrated ability to work across complex data ecosystems. - Proven experience implementing Data Quality initiatives, including the definition of rules and controls, monitoring, validation, and remediation processes. - Advanced knowledge of Data Quality Frameworks, Data Profiling, Data Validation, Data Cleansing, and Data Observability. - Experience with analytical platforms such as Databricks, Snowflake, or equivalent technologies. - Strong experience developing data pipelines using SQL, Python, and modern Data Engineering tools and practices. - Knowledge of Great Expectations and dbt for Data Quality, as well as data lineage, governance, privacy, retention, and anonymization concepts. - Understanding of DataOps, CI/CD, version control, and automated testing practices for data. - Knowledge of Data Catalog, Data Lineage, Metadata Management, Data Stewardship, and Data Governance. - Experience working with Data Products, Data Domains, or domain-oriented architecture models. - Ability to act as a technical reference, influence architectural decisions, and lead technical discussions with both local and global teams. - Advanced English proficiency for effective communication and collaboration in international environments. ### Benefits: - Fully remote work model. - Opportunity to operate as a technical reference for Data Quality within a large-scale data environment. - Exposure to modern Data Engineering, Data Quality, DataOps, observability, and Data Governance practices. - Opportunity to influence technical roadmaps, architectural decisions, and enterprise data standards. - Collaboration with Data Engineering, Data Governance, business, and international teams. - Technical mentorship opportunities and the ability to contribute to organizational maturity in Data Quality. - Hands-on experience with technologies and concepts including Databricks, Snowflake, Python, SQL, Great Expectations, dbt, Data Catalog, Data Lineage, and Metadata Management.