Looking for a job

David Somocurcio

@davidsomocurcio

Lead Data Engineer at Microsoft who optimized analytics pipelines and deployed machine learning models.

At Microsoft, I led a team designing and optimizing data pipelines for analytics dashboards, integrating internal and external data sources.

I developed data architecture using Redshift, Postgres, and Spark, and implemented AWS ECS and S3 solutions to improve data storage and processing.

I deployed machine learning models and optimized data-fetching mechanisms to improve forecasting and query performance. I also streamlined data quality checks and developed monitoring tools for pipeline performance.

At Tompkins International, I supported real-time analytics pipelines and data warehouse integration, using Python, Airflow, and Redshift. Earlier, I contributed to data engineering research at Georgia Institute of Technology and developed software solutions at Fidelity Investments.

Experience

Work history, roles, and key accomplishments

Lead Data Engineer

Microsoft

Jun 2019 - Sep 2026 (7 years 3 months)

Led a team of data engineers to design and optimize data pipelines, ensuring seamless integration across internal and external sources. Developed and maintained data architecture using Redshift, Postgres, and Spark, and implemented AWS ECS and S3 solutions to streamline data storage and processing.

Data Engineer

Tompkins International

Jun 2018 - Dec 2018 (6 months)

Supported the design and implementation of data pipelines for real-time analytics using Python and Airflow. Integrated various data sources into a centralized data warehouse on Redshift and executed ETL processes to transform raw data into actionable insights.

Python Airflow Redshift Pandas Docker Apache Spark SQL Kafka Prometheus Grafana Jira AWS Machine Learning ETL Data Governance Agile

Data Engineering Intern

Georgia Institute of Technology

Jan 2018 - Aug 2018 (7 months)

Contributed to the development of data models and architecture for research projects, emphasizing advanced data structures. Analyzed large datasets using SQL and Python, and supported the integration of local and cloud-based data sources.

SQL Python Data Structures ETL Airflow PostgreSQL S3 AWS RedShift Machine Learning Agile

Software Engineer

Fidelity Investments

Jun 2015 - Jan 2018 (2 years 7 months)

Developed software solutions to enhance data processing capabilities, focusing on efficient algorithms for large financial datasets. Collaborated with cross-functional teams to ensure data integrity in financial reports and designed tools for automated data extraction and transformation.

Data Processing Algorithms Data Extraction data transformation Automation Financial Services