Data Engineer Specialist (Data Quality)
New
J
JobgetherData Engineering
BrazilFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- Advanced English proficiency
- Required Skills
- PythonSQLSnowflakeData engineeringdbtDatabricks
Requirements
- Bachelor’s degree in Technology, Engineering, Mathematics, Statistics, or a related field.
- Solid Data Engineering experience in large-scale corporate analytical environments.
- Proven experience implementing Data Quality initiatives, including the definition of rules, monitoring, validation, and remediation.
- 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.
- Knowledge of Great Expectations and dbt for Data Quality.
- Understanding of data lineage, governance, privacy, retention, and anonymization concepts.
- Knowledge of DataOps, CI/CD, version control, and automated testing practices for data.
- Experience working with Data Products, Data Domains, or domain-oriented architecture models.
- Advanced English proficiency for effective communication and collaboration in international environments.
Responsibilities
- 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.
- Define and implement data quality metrics, validation rules, SLAs, scorecards, and monitoring mechanisms.
- 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.
- Mentor engineers and analysts by sharing technical knowledge and promoting best practices.
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