Data Engineer with 4+ years of experience designing scalable cloud-native data platforms and building enterprise ETL/ELT solutions across Azure, AWS, and GCP environments. Expertise in Databricks, Snowflake, PySpark, Azu…
Data Engineer with 4+ years of experience designing scalable cloud-native data platforms and building enterprise ETL/ELT solutions across Azure, AWS, and GCP environments. Expertise in Databricks, Snowflake, PySpark, Azure Data Factory, BigQuery, Kafka, and distributed data processing for banking, healthcare, and insurance domains. Proven experience delivering analytics-ready data platforms, optimizing large-scale pipelines processing data, enabling real-time streaming analytics, and improving reporting performance for AML, fraud detection, compliance, and operational intelligence initiatives.
<p>Developed metadata-driven ETL frameworks that accelerated onboarding of new source systems and improved engineering productivity.<br>
Built Delta Lake Medallion Architecture supporting AML analytics, fraud monitoring, and regulatory reporting across high-volume financial datasets.<br>
Optimized PySpark transformation pipelines processing 500GB+ daily transaction data, reducing overall pipeline runtime by 25%.<br>
Created finance and compliance reporting datasets in Snowflake and Synapse Analytics, improving audit readiness and reporting turnaround.<br>
Leveraged Microsoft Fabric components including Data Factory, Lakehouse, OneLake, and Warehouse to build scalable ELT pipelines and centralized analytics platforms, improving data integration efficiency and supporting self-service reporting initiatives.<br>
Automated orchestration and monitoring workflows using parameterized ADF pipelines, achieving 99.9% SLA compliance.<br>
Developed Kafka-based streaming pipelines enabling near real-time fraud detection and transaction analytics.<br>
Reduced cloud compute costs through partitioning, caching, workload optimization, and Databricks performance tuning.<br>
Implemented dbt transformation workflows improving data lineage visibility, modularity, and testing coverage.<br>
Automated Azure infrastructure provisioning using Terraform and Azure DevOps CI/CD pipelines, improving deployment consistency across environments.<br>
Collaborated with fraud, risk, and finance stakeholders to deliver analytics-ready datasets and operational dashboards for faster business decision-making.<br>
Enabled advanced analytics and AI-driven use cases by supporting Azure ML, LLMs, NLP, and Generative AI initiatives, leveraging GitHub Copilot for accelerated development and integrating Power BI dashboards for executive-level decision making.</p>
<p>Engineered secure AWS Glue and Redshift pipelines integrating clinical records, laboratory results, APIs, and flat-file healthcare datasets.<br>
Built scalable ingestion workflows processing healthcare data while maintaining HIPAA-compliant validation and encryption standards.<br>
Designed dimensional data models and optimized Redshift tables improving performance for patient and clinical analytics reporting.<br>
Automated near real-time ingestion workflows using AWS Lambda and S3 event-driven architectures, improving downstream data availability.<br>
Implemented reusable validation and reconciliation routines improving consistency across multiple healthcare source systems.<br>
Reduced query latency and improved ETL efficiency through Redshift partitioning, indexing, and workload optimization.<br>
Enforced healthcare data governance standards using metadata tracking, RBAC controls, encryption policies, and audit-compliant practices.<br>
Monitored ETL operations using AWS CloudWatch and proactively resolved workflow failures to maintain highly available processing environments.</p>
<p>Developed cloud-native data pipelines using Dataflow, BigQuery, and Apache Airflow supporting large-scale insurance analytics and reporting initiatives.<br>
Built Pub/Sub streaming architectures enabling near real-time processing of policy transactions, underwriting events, and claims updates.<br>
Designed scalable star and snowflake schema models in BigQuery improving reporting performance and analytical accessibility.<br>
Consolidated claims systems, policy platforms, APIs, and relational database sources into centralized enterprise data repositories.<br>
Developed transformation pipelines converting raw operational data into business-ready datasets for underwriting, risk, and customer analytics.<br>
Improved reporting accuracy through automated validation checks, reconciliation routines, and data quality monitoring frameworks.<br>
Reduced BigQuery storage and query costs using partitioning, clustering, and optimized query execution strategies.<br>
Supported enterprise governance initiatives by implementing lineage tracking, validation frameworks, and role-based access controls.<br>
Delivered integrated analytics datasets powering Power BI and Looker dashboards for operational and claims KPIs.<br>
Collaborated with analytics and business teams to optimize reporting workflows and improve data-driven decision-making.</p>
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