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Moderate competition: metro locations and known employer but role’s niche data-quality tooling limits applicant pool.
Medium — core data-quality skills are transferable, but retail domain preference increases specialization.
High due to mandatory advanced SQL, platform/tool experience, and assessment-verified skills.
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Own validation and testing of data pipelines, ETL/ELT processes, and warehouse/lakehouse data integrity across batch and streaming environments.
Develop and maintain automated data quality frameworks integrated into CI/CD and pipeline orchestration tools.
Lead validation of BI dashboards, AI/ML models, and data migrations ensuring accuracy, compliance, and reliability.
Professional hands-on experience testing data pipelines, ETL/ELT processes, and data warehouse/lakehouse loads.
Advanced SQL proficiency including complex joins, window functions, and aggregate reconciliation queries (verified in interviews or assessments).
Experience with at least one modern data platform (Snowflake, Databricks, Incorta, BigQuery, or Redshift) and one orchestration or transformation toolchain (dbt, Airflow, Informatica, or Spark).
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in automated data quality automation tools such as Great Expectations, dbt tests, or Soda within production CI/CD pipelines.
Familiarity with validating AI/ML models and Conversational BI (text-to-SQL) including deep reasoning.
Exposure to retail data domain (sales, transaction data, inventory, product hierarchies, customer/loyalty data) is strongly preferred.