Implemented event-driven data platform supporting operational and analytical data using Databricks, Airflow, dbt, Python, and AWS services. Delivered analytics-ready datasets and migrated historical data to Databricks. Developed batch and real-time processes with Pyspark, Kinesis, Spark Streaming, Kafka. Modeled data with DBT and developed backend APIs with Flask/FastAPI integrated with Airflow and Terraform. Implemented MLOps framework reducing model production time by 70%, improved data quality, and used CI/CD tools, Docker, Kubernetes. Reorganized data governance with roles and policies using DAMA DMBoK, Power BI, AWS Glue Data Catalog, Databricks.

• Cloud Data Platform: Implemented the Data Platform to support operational and analytical data. Achievements: - Implemented LakeHouse approach. - Migrated historical data, jobs, ETLs and models from Redshift to Snowflake. - Implemented layers for staging (s3, Lambda), processing (EMR, spark, glue, Snowflake, Talend), discovery (Snowflake, Athena), analytics (SageMaker) and visualization (Looker). - Orchestration of processes (airflow, aws step functions). Tools: S3, Glue, AWS EMR, Athena, Lambda, Snowflake, Looker, Redshift, Airflow, SageMaker, Pyspark • DataOps and MLOps Implementation: Implemented a framework that allows the adoption of agile practices throughout the life cycle of a data model and machine learning models by performing automatic deployments in production. Achievements: - Reduced the time to go to production of models by 70%. - Quality indicators in the data were increased (Accuracy, Consistency, Reliability, Completeness, Usability). - Implemented CI/CD tools (Github, terraform, Cloud9, AWS CDK). - Collaborated with data engineers and data scientist to design and implement data structures and models for improved analytics capabilities. Tools: SageMaker, Airflow, Python, SQL. Amazon RDS, Terraform, GitHub, Docker, Kubernetes • Data Governance Implementation: Reorganized the data area in order to have roles such as data architects, data engineers and data governance team. Achievements: - Implemented good practices like defining access policies, Version control, monitoring activity, data auditing and defining retention policies. - Worked closely with different departments (IT, BI, Marketing, Security). - Created and trained the data operations team to support data and ML models in production. - Data Governance Roadmap. - Data Security Roadmap. Tools: DAMA DMBoK, Looker, LookML, AWS Glue Data Catalog, Snowflake • Casino Online 2.0: Led the implementation of the platform that allows customers to live the same experience as the physical casino. Achievements: - The cloud data platform supported this implementation , the amount of transactions increased from 20k to 600k. - Online and physical customer registration was unified across all casino data sources. - The architecture was designed in order to implement promotions, challenges, raffles, benefits directly to the client through the online casino page. - Developed machine learning algorithms to improve predictive analytics and data mining capabilities. Tools: A/B testing, Looker, Agile methodologies, Scrum, Jira, AWS Lambda, Snowflake

Implemented cloud Data Lake for Credicorp Group using Data Mesh and Lakehouse approaches. Migrated data and ETL processes to Azure cloud with Medallion Architecture. Implemented data lineage with Purview and CI/CD with Azure DevOps. Designed data architecture for Yape mobile app, migrating data from AWS to Azure, supporting customer growth from 200k to 2.2M. Automated jobs and pipelines with Databricks and Data Factory, created dashboards with Tableau. Deployed over 20 ML models in batch and real-time on Azure using Databricks, Azure ML, AKS, reducing production time from 7 days to 30 minutes.

• Corporate Data Lake Cloud - Credicorp: Implemented cloud Data Lake to support all companies of Credicorp Group (BCP, Pacificos Seguros, Prima AFP, Mi Banco). Achievements: - Implemented the Data Mesh and Lakehouse approaches for the Credicorp data platform. - Migrated all data from the on premises Data Lake to BigQuery and Google Storage. - Migrated more than 4000 ETL hadoop process to Google Cloud Dataflow and Airflow. - Implement layers por staging, processing and analytics. - Implemented data lineage with DBT. Tools: GCP, Airflow, DBT, Databricks, BigQuery, Python, Kubernetes, Looker • Yape - Data Architecture: Led the data architecture of one of bank's biggest tech project the consist in a mobile application which you can send and receive money totally free just using the cell phone number of your contacts. Achievements: - In charge of the design, implementation and the continuous improvements of Yape data ecosystem that process, transform and provide data to the main data products. - The cloud data lake supports customer increases from 200k to 2.2M. - Implemented datawarehouse with azure synapse. - Automated Jobs and pipelines with DBT. - Implemented a LakeHouse architecture. Tools: Databricks, Spark, Python, Datafactory, Blob Store, DBT, Azure Synapse, Azure Machine Learning, CosmosDB

Recruited and sourced qualified candidates for various positions within the company. Conducted interviews and assessed candidate qualifications. Developed and maintained relationships with hiring managers and clients. Utilized effective communication and negotiation skills to secure placements. Collaborated with team members to achieve recruitment targets.

Collaborated with the marketing team to craft creative, error-free copy for diverse marketing materials, including ads, social media content, and website text. Conducted research to comprehend the target audience and shape resonant messages. Worked with designers and teammates to build cohesive marketing campaigns aligned with brand guidelines.

Implemented and tuned Hortonworks Data Platform and Data Flow for Telefonica's Data Lake. Migrated data from Teradata and Oracle to HDFS, Hive, Impala. Migrated ETL processes to NIFI and Spark, integrated real-time solutions. Deployed over 70 advanced analytical models. Developed LUCA Smart Step for mobility studies using anonymized mobile network data. Developed Unified Relational Model data warehouse standardizing global data. Tools included Azure, Hadoop, Spark, Hive, Kafka, Scala, Python, Power BI, Redash.

• Data Lake Implementation: Implemented and tuning Hortonwork Data Platform (HDP) and Hortonwork Data Flow (HDF) to support Data Lake of Telefonica. Achievements: - Upgraded HDP 1.6 to HDP 3.0. - Migrated 100% of data from Teradata, Oracle to HDFS, HIVE and Impala. - Migrated 100% of ETL process from DataStage to NIFI and Spark. - Integrated Nifi and Spark for real time solutions. - Implemented the guidelines to deploy Machine Learning Models into production in the Data Lake. - Implemented into production more than 70 advanced analytical models. - Designed the data load architecture with hive and spark using CI/CD tools. Tools: Hadoop, Spark, Hive, Impala, Nifi, hdfs, Sqoop, Flume, Kafka, Oozie, Zookeeper, Pyspark, Python, SQL • LUCA Smart Step: Smart Steps uses real behaviors based on the billions of events that occur in Telefonica’s mobile network, anonymized, aggregated and extrapolated to the total population, which allows mobility studies to be carried out. Achievements: - The data had to be anonymized, processed and extrapolated to the entire population. - This solution helped companies such as Jockey Plaza, PromPeru, Interbank and the Breca Group to have a profile of their consumers. Tools: AWS S3, Athena, Lambda, Hive, Spark, Scala, Python, Pytorch, PostgreSQL • URM (Unified Relational Model): It is a data warehouse that aims to standardize the organization’s data at a global level and is used as input for the processes deployed in AURA. (4th platform of Telefonica). Achievements: - Information on 12 entities and 43 dimensions that are part of the global data model was put into production. Tools: Power BI, Redash, Data Warehouse, Python, Scala, Spark, Kafka, nifi, hdfs, yarn, PostgreSQL

Led team to implement eVA Virtual Assistant architecture supporting chatbots with NLP and sentiment analysis for multiple clients. Improved customer satisfaction by 70%. Developed framework to automate ML model deployments using GitLab and Jenkins, increasing deployment efficiency by 350%.

• eVA(everis Virtual Assistant): Lead a team of enginners to implement eVA's architecture to support chatbots for different channels using NLP and sentiment analysis. Achievements: - It supports more than 15 million BCP customer transactions with its Chatbot called 'Arturito'. - Implemented POCs for BCP, Interbank, Belcorp and Telefonica. - Improve 70% of customer satisfactions. - Implemented image recognition in chatbots to make buying cosmetics easier. Tools: Python, Pytorch, Tensorflow, Java, PostgreSQL, IBM Watson, LUIS Microsoft, SQL • Framework to automate Machine Learning models deployments: Developed a framework to deploy Machine Learning models automatically using gitlab and jenkins. - This Framework was used to go into production more than 50 models. - Increased time efficiency of model deployments by more than 350%. Tools: Jenkins, Spark, Python, R, Jupyter Lab, Kubernetes, Docker, GitLab CI/CD, SQL

Managed a portfolio of clients and successfully increased sales revenue for a kitchen equipment company. Built strong relationships with clients and provided exceptional customer service. Utilized effective sales techniques to meet and exceed sales targets. Conducted product demonstrations and provided product knowledge to customers. Collaborated with team members to develop and implement sales strategies.

Implemented data pipelines and maintained data warehouses for clients including Telefonica and Interbank using Java, Python, SQL Server, Oracle. Collaborated with business stakeholders to define data requirements.

• Analytics Platform: Implemented pipelines in clients such as Telefonica and Interbank. Achievements: - Built data pipelines using Java and Python. - Developed and maintained data warehouses using SQL Server and Oracle. - Collaborated with business stakeholders to define data requirements. Tools: Bluemix (IBM Cloud), Jenkins, Git, vagrant, Docker, python, sql, Netezza, IBM DataStage