• 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.

• Own and expand our semantic layer • Lead semantic layer development in dbt as the single source of truth for metrics across dashboards, reports, AI tools, and embedded analytics • Partner with the Senior Data Engineer on data architecture and quality testing for data products • Deprecate legacy reporting methods and drive adoption of the semantic layer as the default • Translate technical concepts and internal terminology into business language so data is intuitive for non-technical users • Build analytics products that drive business value • Lead refreshes and new builds of internal and partner-facing analytics products that help us identify risks and surface opportunities • Support embedded analytics work that brings customer-facing dashboards directly into the product • Partner with Partner Success, Product, and Leadership to gather requirements and design analytics products that answer the question behind the question • Make data trusted, accessible, and easy to use • Own our internal data knowledge base - the centralized source for documentation, metric definitions, and lineage • Create documentation, short-form videos, and dashboard guides that help employees use data confidently • Support cross-functional data power users for knowledge sharing and feedback on data products • Partner on data literacy training and onboarding, including new-hire modules and annual refreshers • Support our AI initiatives • Build and maintain the verified query repository and curated data assets that power our internal AI agents • Monitor agent performance and identify gaps in context or data that can be addressed at the data layer

• Build new analyses and support existing ones using SQL and Python. • Apply software engineering principles like version control and continuous integration to the analytics codebase. • Expand our data warehouse with clean data ready for analysis. • Apply advanced data testing strategies to ensure resulting datastores are aligned with expected business logic. • Implement validation checks and automated testing procedures to manage data quality in your ETL/ELT pipelines. • Work with stakeholders to define business logic and data expectations. • Help drive a change in the usage of data by actively surfacing insights to stakeholders. • Lead initiatives and problem definition, scoping, design, and planning. • Build tools and automation to run data infrastructure. • Manage large-scale data migrations in relational datastores.