This featured candidate specializes in building scalable, cloud-based data pipelines and analytics solutions using Azure, AWS, Databricks, Spark, Python, and SQL to transform complex data into actionable business insights! Please take a moment to review her professional summary:
PROFESSIONAL HIGHLIGHTS
- Designed and developed end-to-end ETL/ELT pipelines using Azure Data Factory, Databricks, PySpark, and SQL for large-scale data integration and analytics initiatives.
- Implemented cloud-based data lake and warehouse solutions on Azure and AWS, improving data accessibility, reliability, and reporting efficiency.
- Automated data processing workflows and optimization strategies, reducing manual effort and improving pipeline performance.
- Collaborated with cross-functional teams to deliver data solutions supporting business intelligence, analytics, and operational decision-making.
- Applied data modeling, data quality, and governance best practices to ensure accurate, secure, and scalable enterprise data platforms.
FUNCTIONAL/TECHNICAL SKILLS
- Data Engineering & ETL Development (PySpark, Spark SQL, Python, SQL)
- Cloud Platforms (Azure, AWS, Databricks, Data Lake, Data Warehouse)
- Data Integration & Orchestration (Azure Data Factory, Airflow, ETL/ELT Pipelines)
- Database Technologies (SQL Server, Snowflake, PostgreSQL, Oracle)
- Analytics & Big Data Processing (Databricks, Delta Lake, Data Modeling, Business Intelligence)
YEARS OF EXPERIENCE: 11 years in data engineering
CAREER GOAL: Seeking a Data Engineer role where she can leverage cloud technologies, big data platforms, and analytics expertise to build scalable data solutions and drive business value
PREFERRED EMPLOYMENT TYPE: Contract, Contract-to-Hire, Corp-to-Corp