GCP - Health Care Company Data Migration:- Create from scratch the architecture for a Data Warehouse migration from SQL Server to Bigquery using Airflow + Dataflow- Model the target Data Warehouse schema to account for optimization of costs, scalability and performance- Create the Airflow DAGs to handle the orchestration of the different tables to be migrated and account for dependencies- Achieve the migration of a 5 TB data warehouse in less than 8 hours, complying with the SLA required from the customerGCP - Pharma Company Real Time migration:- Set up the logical replication in Cloud SQL Postgres to backfill and live capture changes in the source database. Challenges addressed in the configuration here include 1+ billion record tables, tables with no keys, indexes with different orders- Set up a Terraform module to comply with the SLAs required via alerts sent to Pager Duty- Execute the migration of the database and address issues in the migration (data types, costs,staleness, etc...)- Close the project giving the customer a fully synced Operational Database and a Data Warehouse with minimal latency

Tech Stack: AWS, Hudi, S3, Postgres, Spark, CloudFormation and CDKGoals: Create from scratch a datalake from ingestion, data quality, data modeling, storage, security and governance to be used by data scientists, stake holders and land data for our applications that need that data.• Architect the data lake to include items like cataloging, governance, security, and data access in a clear fashion using S3, Glue, and other AWS Services for the business to work• Implement batch and streaming data pipelines to provide sales estimations for different products for our customers so they are able to create their marketing and selling strategies.• Implement EMR jobs to process incremental data capture with Apache Hudi to process more than 100M sales estimations, amazon ranking booster keywords, and demand predictions for our customers to give them a competitive advantage over other sellers• Use Apache Spark + Hudi over EMR and orchestration via Step Functions to ingest data from different sources and create a Modeled Zone with a Data Lakehoue architecture• Migrate a main operational Database without downtime using Amazon DMS to the Data Lake so we could decommission it when the applications wrote directly to the Data Lake

Tech Stack: Kafka, Postgres, ElasticSearch, Cassandra, Python, Spark, Airflow, AWS, and KubernetesCreate and maintain an elastic Streaming Data Pipeline built with Kafka and Microservices built on top of Docker and Kubernetes to ingest data and model it as FHIR objects in ElasticSearch to make the data searchable by different applications in the company.Create the data lake ingestion pipelines from OLTP sources and ElasticSearch using Apache Airflow to send the data to S3 to be processed.Create Spark scripts to process data, clean data, and create analytical reports related to medication consumption metrics by patients.

The courses taken include:- Pattern Recognition- Artificial Intelligence techniques- Multivariate Data Analysis- Databases and Data Mining- Data Visualization- Supercomputing for Big Data

Lead a team of engineers to make them grow as we worked in the company, the roles includes:- Giving technical feedback and a plan to improve skills as needed- Help the Junior engineers to level up their game and coding skills- Provide 1:1 sessions and space to get feedback from the team- Create career paths for the team to achieve their goals- Create scalable architectures and solutions for our customers

Tech Stack: Airflow, Kafka, Cassandra, ElasticSearch, GCP, AWSCreate data pipelines and data modeling for companies located in the US. The responsibilities varied from customer to customer but normally were about creating batch pipelines with Airflow, modeling Data Marts, creating Streaming Pipelines and set up the solutions in the cloud for Production usage

Provide best practices and implement projects with IBM Watson technology to give our customers the best value for this AI technology. The responsibilities include:- Understand the Watson technology to give the customer the right expectations and scope- Deliver IBM Watson technology - Train IBM Watson and measure the performance over the training, blind and test set and give advise to improve the system- Understand and give the best practices to obtain, transform and load the data to IBM Watson.

Business Intelligence Consultant. The labors include design and construction of analytical models that allow organizations to become more efficient while optimizing their business processes, construction of data extraction, transformation and loading (ETL) processes from different sources, creation of data quality processes and construction of Dashboards and Scorecards that allow organizations to measure the accomplishment of operational and strategic objectives.