• 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.

• Maintain a full portfolio of new CRM clients, serving as their data point of contact throughout implementation. • Conduct data imports by preparing complex data import rules for software engineers. • Perform data validation checks throughout the process to ensure data integrity and accuracy, including pre-migration audits and post-migration reconciliation. • Examine source Excel data to identify its structure, quality, and potential issues like inconsistencies, missing values, or non-standard entries. • Document the entire migration process, including data maps, transformation logic, and any issues encountered, and communicate with business stakeholders and development teams to ensure all requirements are met. • Address and resolve any issues that arise during or after the migration, such as data import errors or data discrepancies. • Manage all projects efficiently by keeping relevant internal systems accurate and up to date, including recording progress in ChurnZero, ZenDesk, JIRA, Salesforce, and other company systems. • Collaborate effectively with internal cross-functional teams (e.g., Sales, Implementation, Support) to ensure alignment on client strategy, project status, and a cohesive, unified service experience throughout the migration lifecycle.