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Bidhi Khanal

@bidhikhanal

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Business & Data Analyst specializing in cloud-based analytics, SQL, Python, and healthcare data governance.

United States

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What I'm looking for

I seek an analytics-focused role where I can build cloud-based data solutions, improve data quality, and partner with stakeholders in a collaborative, regulated healthcare or enterprise environment.

I am a Business & Data Analyst with 4+ years delivering enterprise analytics solutions across healthcare and Fortune 500 environments. I translate complex business requirements into scalable cloud-based data solutions using SQL, Python, and AWS.

I design KPI frameworks, data governance models, and modernization strategies that improve data quality and reporting efficiency in regulated environments like HIPAA and GDPR. I lead stakeholder workshops and As-Is/To-Be analyses to align analytics with business goals.

My technical work includes building SQL transformations, Python validation frameworks, and optimizing Snowflake/Redshift environments to accelerate KPI access and reduce dashboard load times. I have driven measurable impact such as improving clinical data accuracy by 30–40% and enabling faster processing of large-scale healthcare datasets.

I own end-to-end deliverables from BRDs and user stories to UAT leadership, partnering cross-functionally with finance, supply chain, compliance, and cloud architecture teams to ensure secure, performant, and cost-effective analytics solutions.

Experience

Work history, roles, and key accomplishments

Current

Business Analyst / Data Analyst

Current

Cardinal Health

Dec 2022 - Present (3 years 2 months)

Partnered with finance, supply chain, and compliance stakeholders to deliver scalable SQL/Python/AWS analytics solutions that improved clinical data accuracy by 40% and accelerated access to enterprise KPIs.

SQL Python AWS Snowflake Redshift Data Modeling Data Governance

Business Analyst / Data Analyst

Spring Health

Mar 2021 - Oct 2022 (1 year 7 months)

Developed and supported Snowflake-based analytics pipelines and automated data quality checks, improving patient engagement data accuracy by ~30% and reducing dashboard load times.