About the Role:
Work Setup Work From Home Must be willing to report onsite
1–2 times per month
as needed
Experience Required Minimum
2 years
of experience in
Data Quality, Data QA, or Data Analysis
Key Responsibilities Perform data quality validation to ensure data accuracy, completeness, consistency, and validity. Validate datasets using SQL and identify data issues, discrepancies, and anomalies. Develop and execute basic Python scripts for data testing and automation. Support validation of structured and unstructured datasets. Review and validate ETL/ELT data pipelines and data transformations. Work closely with cross-functional teams to resolve data quality issues. Ensure data integrity across databases and cloud-based platforms. Document findings, recommend improvements, and support continuous data quality initiatives.
Qualifications:
Required Minimum
2 years
of experience in Data Quality, Data QA, or Data Analysis. Strong SQL skills for data validation, querying, and analysis. Working knowledge of Python for data testing and automation. Solid understanding of data quality principles: Accuracy Completeness Consistency Validity Experience working with ETL/ELT processes. Familiarity with structured and unstructured data. Knowledge of cloud platforms such as AWS or Azure. Experience working with databases such as MongoDB.
Preferred Qualifications Experience with data validation frameworks (e.g., Great Expectations, dbt Tests). Familiarity with vector databases and RAG pipeline evaluation. Exposure to AI/ML or Large Language Model (LLM) systems. Experience evaluating AI-generated outputs. Background in healthcare or life sciences data. Experience working in an Agile environment.
or continue with email
Work Setup Work From Home Must be willing to report onsite
1–2 times per month
as needed
Experience Required Minimum
2 years
of experience in
Data Quality, Data QA, or Data Analysis
Key Responsibilities Perform data quality validation to ensure data accuracy, completeness, consistency, and validity. Validate datasets using SQL and identify data issues, discrepancies, and anomalies. Develop and execute basic Python scripts for data testing and automation. Support validation of structured and unstructured datasets. Review and validate ETL/ELT data pipelines and data transformations. Work closely with cross-functional teams to resolve data quality issues. Ensure data integrity across databases and cloud-based platforms. Document findings, recommend improvements, and support continuous data quality initiatives.
Qualifications:
Required Minimum
2 years
of experience in Data Quality, Data QA, or Data Analysis. Strong SQL skills for data validation, querying, and analysis. Working knowledge of Python for data testing and automation. Solid understanding of data quality principles: Accuracy Completeness Consistency Validity Experience working with ETL/ELT processes. Familiarity with structured and unstructured data. Knowledge of cloud platforms such as AWS or Azure. Experience working with databases such as MongoDB.
Preferred Qualifications Experience with data validation frameworks (e.g., Great Expectations, dbt Tests). Familiarity with vector databases and RAG pipeline evaluation. Exposure to AI/ML or Large Language Model (LLM) systems. Experience evaluating AI-generated outputs. Background in healthcare or life sciences data. Experience working in an Agile environment.
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