• Perform migration activities of Alteryx workflows to AWS; • Develop and refactor data pipelines using AWS Glue; • Adapt processes and business rules to a cloud-native architecture; • Ensure the quality, performance, and scalability of pipelines; • Provide technical support to the team with specialized Alteryx expertise; • Participate in the analysis, documentation, and optimization of ETL processes.

• Develop and maintain batch and streaming data pipelines in Databricks using PySpark and Spark SQL; • Implement pipelines following the established Medallion architecture (Bronze, Silver, and Gold); • Assist in migrating legacy pipelines and jobs from tools such as IBM DataStage, Azure Data Factory, and Azure Synapse Analytics to Databricks Workflows; • Help migrate routines and notebooks from Databricks on Azure to AWS; • Develop and version notebooks and code using Databricks Repos and Git; • Implement basic tests and data quality routines in pipelines; • Support dimensional modeling of the Data Warehouse (Star Schema) with the architecture team; • Collaborate with the DevOps team to automate deployments and CI/CD; • Participate in implementing data governance with Unity Catalog; • Monitor and provide support for production data pipelines.

• Build the new corporate Lakehouse using Databricks on AWS; • Develop batch and streaming data pipelines with PySpark and Spark SQL; • Create and maintain the Medallion architecture (Bronze, Silver, Gold) in Delta Lake; • Participate in designing the new Data Warehouse data model; • Migrate data, pipelines, and routines from the current Databricks on Azure environment to AWS; • Migrate jobs and integrations from IBM DataStage, Azure Data Factory, and Azure Synapse Analytics; • Migrate Databricks workspaces between Azure and AWS environments; • Configure and operate data governance using Unity Catalog and Odin; • Implement monitoring, testing, and data quality; • Work alongside DevOps on deployment automation and CI/CD; • Version notebooks, pipelines, and code using Git and Databricks Repos.

• Design, build, and maintain scalable data pipelines that ingest, transform, and validate large volumes of data across multiple sources and channels. • Improve the scalability, reliability, and performance of our data pipelines to support rapidly growing workloads and new data streams. • Contribute to the design and implementation of our Data Lake architecture, enabling reliable data storage and reuse across teams. • Manage and optimize data ingestion workflows, including data collected from web scrapers, third-party vendors, and internal systems. • Monitor pipeline health, investigate incidents, and implement improvements to increase system reliability and observability. • Support the onboarding and integration of new AI channels and data sources into the platform. • Collaborate with teams across the organization to ensure data generated by different systems can be reused effectively for analytics and business intelligence. • Identify and resolve performance bottlenecks in distributed systems, including rate limiting, concurrency, and throughput constraints. • Advise engineering and product teams on data architecture, data quality, and best practices for managing scalable data workflows. • Continuously evaluate and improve our data platform to support the company’s rapid growth and evolving product needs.