• Develop, maintain and evolve data ingestion and transformation pipelines following a medallion architecture; • Build and monitor integrations between the data lake and external applications (Octadesk, Salesforce and others); • Implement observability mechanisms for proactive failure detection, performance monitoring and cost control; • Collaborate with product, BI and engineering teams to understand needs and design scalable data solutions; • Document processes, pipelines and technical decisions to facilitate maintenance and onboarding of new team members.

• Design, develop, and maintain scalable data pipelines using Databricks and Python • Build and optimize ETL workflows for structured and unstructured data • Work with Databricks Lakehouse architecture and implement best practices • Manage and implement data governance using Unity Catalog • Integrate AWS data services such as S3, IAM, VPC, and other relevant services • Collaborate with cross-functional teams to deliver high-quality data solutions • Build dashboards and support data visualization requirements • Ensure data quality, reliability, and performance optimization

• Build data pipeline, ELT and infrastructure solutions • Create automated monitoring, auditing and alerting processes • Work with business and application teams to implement data strategies • Design, develop, implement, and optimize ETL processes • Improve operations, monitoring and performance of data warehouse • Influence and communicate with stakeholders • Redesign and optimize existing data processes using AI • Live by and champion company values

• Develop and maintain ETL/ELT data pipelines in high-volume, complex environments using Apache Spark (Scala and Python) and AWS; • Contribute to the evolution and expansion of the Analytics Platform, focusing on Big Data architectures and technologies; • Gather, analyze, and translate business requirements into technical data solutions; • Design and maintain analytical data models, metrics, and KPIs; • Integrate multiple data sources, including streams (Apache Kafka / MSK) and SQL and NoSQL databases; • Develop, operate, and evolve data pipelines in production environments; • Ensure scalability, performance, reliability, data quality, and governance; • Prepare, cleanse, and make data available for analytical and operational consumption; • Automate the creation, configuration, and monitoring of data pipelines and environments; • Manage SLAs, handle failure remediation, and resolve incidents in data flows; • Work collaboratively in agile squads, supporting technical decisions and promoting data engineering best practices; • Provide technical mentorship and share knowledge within the team.