Senior Data Engineer

- Designed and implemented scalable ELT pipelines using Snowflake, dbt, Apache Airflow, and dlt, delivering reliable production data workflows for enterprise analytics. - Developed high-performance data transformations with SQL and Python, leveraging incremental models, automated testing, and query optimization to improve pipeline efficiency. - Built and maintained Snowflake data models, orchestration workflows, and data quality controls across development, QA, and production environments. - Integrated data from SQL Server, APIs, and file-based sources into Snowflake, supporting bronze, silver, and gold data layers for analytics and reporting. - Developed Stre

- Engineered end-to-end data pipelines using Snowflake, Python, and SQL, integrating data from multiple enterprise systems into centralized analytical repositories. - Automated ELT workflows with Apache Airflow and dbt, improving pipeline reliability, scheduling, and operational efficiency. - Developed dimensional models and curated reporting datasets that powered Power BI dashboards and business intelligence initiatives. - Troubleshot production data issues, optimized query performance, and partnered with analysts and stakeholders to deliver trusted, high-quality datasets.

- Built ETL and ELT pipelines using Python, SQL, Azure Data Factory, and cloud data platforms to prepare structured datasets for analytics and machine learning. - Performed exploratory data analysis, feature engineering, and statistical analysis to identify trends and support predictive modeling initiatives. - Developed machine learning models and evaluated model performance while collaborating with stakeholders to solve operational business problems. - Created Power BI dashboards and automated reporting solutions, translating complex datasets into actionable business insights.

- Developed and maintained Power BI dashboards for multiple departments, enabling operational reporting and KPI visibility. - Engineered ETL processes and wrote SQL queries and stored procedures in SQL Server to support recurring analytics needs. - Validated source data, resolved reporting issues, and partnered with business users to improve data accuracy and usability.