
Azure Data Engineer with hands-on experience designing and building cloud-native data platforms using Azure Synapse, Databricks, Data Factory, and ADLS. Skilled at developing scalable, metadata-driven pipelines that support complex ingestion, transformation, and analytics workloads across healthcare environments. Strong track record in structuring data lakes, implementing ELT/ETL frameworks, and optimizing performance for large-volume datasets. Adept at diagnosing data quality issues, validating complex business rules, and partnering with architects and SMEs to deliver reliable, secure, and compliant data solutions. Brings a practical, engineering-first mindset with a focus on automation, observability, and production-ready design.
Data Engineering & Pipeline Development
ETL/ELT design, metadata-driven pipeline architecture, orchestration patterns, incremental and historical data loads, data quality validation, error handling frameworks, logging and monitoring, CI/CD for data pipelines
Programming & Frameworks
Python (PySpark, data processing libraries), SQL (T-SQL, Synapse SQL), Spark, Delta Lake, JSON/XML processing, API integrations
Data Modeling & Architecture
Dimensional modeling, unified data models, schema design, partitioning and indexing strategies, lakehouse architecture, Synapse dedicated and serverless models, healthcare data analysis fundamentals
Analytics & Governance
Data governance frameworks, lineage tracking, data classification, RBAC, PHI handling, compliance alignment, metadata management, Power BI dataset support
Tools & DevOps
Git, Azure DevOps, CI/CD pipelines, Terraform (optional for infra-as-code), monitoring dashboards, automated alerting
Soft Skills
Cross-functional collaboration, technical leadership, stakeholder communication, requirements translation, documentation, sprint-based delivery