Esau Reyes

Data Engineer Director

Arcus•  April 2021 - Present

Enabling a data-driven organization. Setup the right cloud infrasctructure for our data lake & data warehouse. Develop streaming and batch pipelines to feed the data platform. ETL´s automation for data cleansing and integration. Star schema & Snowflake schema for analytics. Provide the necessary infrastructure and leverage the right tools for analysis, visualization and reporting. Migrate legacy architectures to make them scalable, highly available and resilient. Leading several PoC of services and technologies. Implement and lead the skilling up program for the team. Tools: Python, PySpark, SQL, AWS S3, AWS Lambda, AWS Glue, AWS Kinesis, AWS CloudWatch, AWS DynamoDB, AWS RDS, Redshift, Athena, QuickSight, JIRA, Linux, Git, API's, Shell Script.

Data Architect

Globant•  November 2020 - April 2021

Agile methodology. Python and Pyspark as main language. Develop scalable Big Data pipelines for streaming data. Data warehouse, data lakes and NoSQL databases. Improve the current architecture taking advantage of AWS services. Migrate and optimaze current serverless ETL processes with AWS Lambda. Standardize code and creation of internal libraries in python. Develop and configure logs for ETL processes. Orchestrate data pipelines through different cloud services. Point of contact with the client, understand their needs, bring the right solution and meet the deadlines. Tools: Python, PySpark, AWS S3, AWS Lambda, AWS Glue, AWS Kinesis, AWS CloudWatch, AWS DynamoDB, JIRA, Linux, Git, API's, Shell Script.

Data Engineer

Procter and Gamble•  November 2018 - October 2020

Agile methodology. Python as main language. Develop scalable and robust Big Data pipelines in Databricks. Pipelines orchestration with Azure Data Factory. ETL processes development/optimization/migration/scale with Python, PySpark, Hive and SQL. Data warehouse and data lakes environments. Setup of CI/CD environment and some other DevOps task. Develop a scalable and automated solution to extract data from for several data sources on the web like Google API’s, weather API’s, demographic data, etc. Work closely with analysts/data scientist to deliver the right data solutions. Tools: Python, Pyspark, SQL, Hive, Azure Data Factory, Azure Data Lake, Azure DevOps, Databricks, Delta Lakes, Power BI, Git, API's, cURL, Shell Scripts.