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Adam Brown

@adambrown3

Senior Analytics Lead at Skedulo who improved data quality workflows and delivered reliable reporting layers in Looker and Tableau.

United States

Message

At Skedulo, I review business data across CRM, advertising, web analytics, and operational sources, and deliver reporting layers in Looker and Tableau. I also use SQL and dbt to identify and resolve data quality issues while following data privacy and PII protocols.

At Greenway Health, I analyzed clinical, operational, marketing, and patient data and built PostgreSQL schemas, reporting datasets, and data pipelines. Earlier, as a Junior Data Analyst at Zappos, I reviewed marketing, order, and fulfillment data and supported data quality assurance and audit projects.

Experience

Work history, roles, and key accomplishments

Senior Analytics Lead

Skedulo

Aug 2020 - Apr 2026 (5 years 8 months)

Reviewed and analyzed extensive business data sets for accuracy, completeness, and relevance across CRM, advertising, web analytics, and operational sources. Delivered reliable reporting layers in Looker and Tableau, and ensured compliance with data privacy and PII protocols.

SQL DBT Looker Tableau Data Quality PII Data Privacy Data Governance Data Lineage Metadata Management CI CD GitHub Actions KPI Definition Data Pipelines ETL ELT Data Storytelling Python Data Modeling QA Testing

Senior Data Analyst

Greenway Health

Jul 2017 - Jul 2020 (3 years)

Analyzed large business data sets from clinical, operational, marketing, and patient systems using SQL, Python, ELT/ETL, and data modeling. Performed document review and data validation for sensitive material, applying data privacy, security, governance, and HIPAA-aligned controls.

SQL Python ETL ELT Data Modeling PostgreSQL Data Quality Data Privacy HIPAA Data Governance Data Lineage Metadata Management Data Pipelines Data Validation Data Reconciliation Anomaly Detection

Junior Data Analyst

Zappos

Jun 2015 - Jun 2017 (2 years)

Reviewed marketing, order, and fulfillment data for accuracy, completeness, and relevance using SQL dashboards, reporting components, APIs, and data pipelines. Supported document review, data quality assurance, testing, and audit projects by flagging anomalies and resolving inconsistencies.