Data Quality / Data Governance Specialist
Location: MexicoFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- +6 years
- Required Skills
- AWSPythonSQLData engineeringSparkAWS Lambda
Requirements
- 5+ years of experience in Data Governance, Data Quality, or Data Engineering-related roles.
- Proven experience implementing (not just documenting) data governance frameworks.
- Strong hands-on experience with data quality frameworks and validation strategies.
- Strong hands-on experience with data contracts and schema enforcement.
- Strong hands-on experience with data observability tools and practices.
- Experience working with batch architectures (Medallion architecture preferred).
- Experience working with real-time/streaming data pipelines.
- Proficiency in SQL and Python.
- Proficiency in distributed data processing tools such as Spark.
- Experience with modern data stack technologies: AWS (Kinesis, Lambda, SQS/SNS).
- Experience with modern data stack technologies: Apache Iceberg or similar table formats.
Responsibilities
- Implement and operationalize data governance frameworks, policies, and standards across the organization.
- Move beyond documentation to ensure real enforcement of governance practices within data pipelines and systems.
- Define and enforce data contracts between producers and consumers.
- Establish ownership, stewardship, and accountability models for data assets.
- Design and implement data quality frameworks to ensure accuracy, completeness, and consistency of data.
- Define and monitor data quality rules and SLAs across datasets.
- Implement and manage data observability tools to track data health, lineage, and anomalies.
- Partner with engineering teams to integrate quality checks into pipelines.
- Ensure data quality and governance across batch processing architectures and real-time/streaming systems.
- Implement validation and quality checks across all layers of the data lifecycle.
- Collaborate on architecture decisions to embed governance and quality by design.
- Work hands-on with modern data technologies, including Python, SQL, Spark, AWS services such as Kinesis, Lambda, SQS/SNS, and data lake/table formats such as Apache Iceberg.
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