• Partner directly with customers to identify data challenges and design technical solutions - connecting data sources, configuring integrations, automating recurring workflows, and deploying custom reports • Deploy and optimize AI-powered tools and workflows for customers; educate customers and internal teams on prompt engineering, LLM capabilities, and best practices for leveraging AI in their data operations • Build and extend internal tooling (importers, parsers, and reporting pipelines) to reduce manual burden on the Customer Experience team and improve platform reliability • Execute bespoke data operations such as custom SQL reports, bulk data operations, and backend queries for customers with unique data needs • Own API schemas, ingestion cadence, error handling, Snowflake data shares, and database connections • Identify repeatable patterns across customers and translate them into product requirements, filing tickets and partnering with Engineering to productize solutions • Support internal engineering workflows as a secondary function by helping to triage and resolve quality-of-life bugs and enhancements that are too small for core Engineering but directly impact the Customer Experience team and customers • Carry a light book of data parsing work alongside your teammates to stay grounded in the team's day-to-day workflows and build supporting solutions

• Design, develop, and deploy scalable AI, machine learning, and advanced analytics solutions to drive business value • Build and maintain robust data pipelines, data models, and enterprise data assets that support reporting, analytics, and AI initiatives • Partner with business stakeholders to translate requirements into data-driven solutions and actionable insights • Implement data governance, quality, security, and responsible AI practices to ensure trusted and compliant use of enterprise data • Support the development and enhancement of AI capabilities through predictive modeling, automation, and continuous innovation • Play a key role in advancing Carrier's AI and data transformation strategy through innovative analytics and AI solutions • Support high-visibility, enterprise-scale initiatives and programs that directly impact business performance and decision-making • Collaborate with global cross-functional teams across business and technology functions to deliver scalable digital solutions • Contribute to strategic programs with significant business impact while gaining exposure to enterprise data, analytics, and AI leadership initiatives

• Serve as a subject matter expert for analytics engineering processes, tools, and best practices while providing guidance to junior team members. • Lead preparation, validation, and integration of complex 1st party, 3rd party, and internal datasets to support advanced analytical solutions. • Establish and enforce quality standards to ensure analytical outputs are accurate, reproducible, and aligned with client requirements. • Coach Associate and Analytics Engineers through technical reviews, workflow guidance, and skills development. • Drive automation, standardization, and continuous improvement initiatives to improve scalability and operational efficiency. • Lead technical execution for complex client engagements, coordinate across teams, and proactively manage delivery risks. • Collaborate closely with Data Science, Customer Success, and Product teams to align analytical execution with business objectives and client expectations

• The Data Analytics Engineer designs, develops, and supports cloud and on-premises data warehouse solutions that enable reporting, business intelligence, and analytics. • This role builds and automates data pipelines, data marts, and data lake integrations using technologies such as Snowflake, SQL Server, AWS, Python, Airflow, and DBT while ensuring data quality, performance, and reliability. • The Data Analytics Engineer collaborates across teams to deliver scalable data solutions, optimize existing processes, and support the organization's long-term data strategy through Agile development and continuous improvement practices. • Develop, maintain, and optimize cloud and on-premises data platforms using Snowflake and Microsoft SQL Server, ensuring reliable, high-performing data warehouse, mart, and lake solutions. • Build and support analytics-ready datasets for reporting and business intelligence while troubleshooting and improving data platform performance, reliability, and accessibility. • Design, develop, and maintain scalable data pipelines and ETL/ELT workflows using Airflow, DBT, Airbyte, SSIS, SQL, Python, and Snowflake technologies to support data integration, automation, and analytics. • Support Agile data engineering practices by building Airflow DAGs, leveraging Snowflake capabilities, and managing CI/CD, database migrations, and source control processes using AWS and Git-based tools. • Support and maintain AWS cloud and on-premises data environments, ensuring secure, reliable, and high-performing data platforms through effective monitoring and administration. • Collaborate with business and technical stakeholders to deliver scalable analytics solutions while identifying and resolving data quality, integrity, and performance issues.