Senior Data Engineer with 6+ years architecting enterprise-scale cloud data platforms across banking,
healthcare, and retail. Deep expertise in Azure Databricks lakehouse architecture, real-time streaming,
and regulatory…
Senior Data Engineer with 6+ years architecting enterprise-scale cloud data platforms across banking,
healthcare, and retail. Deep expertise in Azure Databricks lakehouse architecture, real-time streaming,
and regulatory-compliant infrastructure (PCI-DSS, HIPAA, SOC 2). Proven track record processing 4B+
daily transactions, cutting cloud costs by 40%, and delivering 99.7% SLA across mission-critical
pipelines. Skilled in PySpark, Delta Lake, dbt, Kafka, and modern data stack tooling.
<p>Architected Azure Databricks Medallion lakehouse across retail, commercial, and wealth lines.<br>
Cut data latency from 24 hrs to under 15 min.<br>
Built 120+ PySpark and ADF pipelines integrating 25 source systems (core banking, CRM, trading).<br>
Sustained 99.7% SLA across 38 downstream consumers.<br>
Delivered PCI-DSS tokenization framework using Azure Key Vault and column-level encryption.<br>
Achieved zero audit findings in 2023 and 2024.<br>
Automated DFAST regulatory reporting for Federal Reserve stress testing.<br>
Reduced report prep from 5 days to 4 hours.<br>
Adopted Delta Lake ACID and time-travel features.<br>
Cut Finance reconciliation effort by 70%.<br>
Optimized Spark execution plans and cluster configs.<br>
Drove 40% cost reduction ($280K/yr) with 55% higher throughput.<br>
Introduced dbt transformation layer.<br>
Raised SQL test coverage from 12% to 85%.<br>
Mentored 3 junior engineers.</p>
<p>Architected lakehouse processing 4.2B daily transactions and drove $280K/year in cloud cost savings., Architected Azure Databricks Medallion lakehouse across retail, commercial, and wealth lines — cut data latency from 24 hrs to under 15 min., Built 120+ PySpark and ADF pipelines integrating 25 source systems (core banking, CRM, trading); sustained 99.7% SLA across 38 downstream consumers., Delivered PCI-DSS tokenization framework using Azure Key Vault and column-level encryption — zero audit findings in 2023 and 2024., Automated DFAST regulatory reporting for Federal Reserve stress testing — reduced report prep from 5 days to 4 hours., Adopted Delta Lake ACID and time-travel features — cut Finance reconciliation effort by 70%., Optimized Spark execution plans and cluster configs — drove 40% cost reduction ($280K/yr) with 55% higher throughput., Introduced dbt transformation layer and raised SQL test coverage from 12% to 85%; mentored 3 junior engineers.</p>
<p>Engineered HIPAA-compliant pipelines processing 850M+ medical claims annually from 300+ payer and provider partners into Azure Synapse for population health analytics.<br>
Built real-time member eligibility platform on Event Hubs and Stream Analytics.<br>
Handled 120K events/sec.<br>
Reduced eligibility errors by 34%.<br>
Implemented Data Vault 2.0 enterprise claims warehouse with full historization.<br>
Passed all bi-annual CMS compliance reviews.<br>
Developed PHI de-identification pipeline (HIPAA Safe Harbor) on Databricks.<br>
Enabled secure data sharing with external research partners.<br>
Productionized 8 ML models for fraud detection and readmission risk using PySpark feature engineering.<br>
Served 2M+ members.</p>
<p>Delivered HIPAA-compliant pipelines processing 850M+ claims/year and cut pipeline runtime by 52%., Engineered HIPAA-compliant pipelines processing 850M+ medical claims annually from 300+ payer and provider partners into Azure Synapse for population health analytics., Built real-time member eligibility platform on Event Hubs and Stream Analytics — handled 120K events/sec and reduced eligibility errors by 34%., Implemented Data Vault 2.0 enterprise claims warehouse with full historization — passed all bi-annual CMS compliance reviews., Developed PHI de-identification pipeline (HIPAA Safe Harbor) on Databricks — enabled secure data sharing with external research partners., Productionized 8 ML models for fraud detection and readmission risk using PySpark feature engineering — served 2M+ members.</p>
<p>Led on-premises to ADLS Gen2 migration of retail sales, inventory, and supply chain data via Azure Data Factory.<br>
Built PySpark ETL pipelines integrating data from 80+ store systems, e-commerce platforms, and third-party logistics partners to power omnichannel analytics.<br>
Developed 200-rule automated data quality framework (completeness, accuracy, referential integrity).<br>
Cut downstream reporting errors by 45% across merchandising teams.<br>
Delivered self-service analytics layer on Azure Synapse for category managers and supply chain analysts.<br>
Reduced ad-hoc data requests to engineering by 60%.<br>
Designed star-schema dimensional model for sales, inventory, and pricing analytics supporting 5,000+ store locations and 100M+ weekly transactions.</p>
<p>Migrated 15 TB of retail data to Azure 3 weeks ahead of schedule, supporting 5,000+ stores., Led on-premises to ADLS Gen2 migration of retail sales, inventory, and supply chain data via Azure Data Factory., Built PySpark ETL pipelines integrating data from 80+ store systems, e-commerce platforms, and third-party logistics partners to power omnichannel analytics., Developed 200-rule automated data quality framework (completeness, accuracy, referential integrity); cut downstream reporting errors by 45% across merchandising teams., Delivered self-service analytics layer on Azure Synapse for category managers and supply chain analysts — reduced ad-hoc data requests to engineering by 60%., Designed star-schema dimensional model for sales, inventory, and pricing analytics supporting 5,000+ store locations and 100M+ weekly transactions.</p>
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