• Develop and maintain ETL processes to support data ingestion, transformation, and integration. • Work with various file formats (TXT, CSV, XML, JSON, YAML) to ensure seamless data processing. • Implement and optimize ETL workflows using EasyMorph, Databricks, Azure Data Factory, Talend, and Apache Nifi. • Write clean, efficient, and maintainable code using Python, C#, or Java. • Ensure data quality, accuracy, and integrity across ETL pipelines. • Collaborate with data engineers and analysts to define and refine ETL requirements. • Troubleshoot and resolve ETL failures, ensuring minimal downtime and impact. • Follow test-driven development (TDD) methodologies to improve reliability and maintainability. • Maintain and document ETL processes, providing insights and recommendations for improvements.

• Partner with product managers, marketers, and engineers to define, implement, and QA Segment tracking plans, ensuring consistent and reliable event data across all surfaces (web, mobile, backend) • Act as a technical owner for Segment event pipelines, monitoring schema drift, naming violations, destination delivery issues, and Protocols violations • Develop and manage custom mappings, traits, and transformations in Segment (including Segment Functions) to power downstream destinations like Braze, Amplitude, and Snowflake • Enable the marketing team to activate audiences and campaigns by ensuring accurate and timely data flows to destinations • Collaborate with data platform engineers to scale our Airflow-based orchestration and build testing frameworks for event and customer data quality • Conduct periodic audits, validation tests, and regressions to ensure data completeness and integrity across environments • Maintain documentation, event naming conventions, and cross-functional education efforts to drive better data literacy and consistency in instrumentation

• Able to build complex visualizations (dashboards) using multiple charts to tell a story • Able to develop complex SQL queries (e.g., UNION, subqueries, CTEs, etc.) • Proficient in at least one data visualization tool (e.g., Metabase, Looker, Tableau, etc.)

• Assist in maintaining scalable, well-documented dbt models that support regulatory reporting and platform safety monitoring. • Help build and optimize data pipelines in dbt, ensuring they meet the high standards of auditability required for compliance. • Collaborate with legal, policy, and engineering teams to help translate complex transparency requirements into technical data solutions. • Implement data definitions and documentation standards to improve data quality and support long-term maintainability of our safety metrics. • Participate in structured reviews of existing analytics infrastructure to identify performance improvements and alignment with reporting needs. • Support the development of our self-service ecosystem by contributing to intuitive semantic layers that serve a broad stakeholder base. • Help implement validation frameworks that enable performance tracking and early detection of data quality issues.