• Responsible for designing, building, and maintaining data pipelines, data integration processes, and data infrastructure. • Collaborate closely with data scientists, analysts, and other stakeholders to ensure efficient data flow and support data-driven decision making across the organization.

• Work collaboratively with data engineers and other partners in the messaging space • Build reliable, scalable data pipelines using Apache Spark/Flink • Power analytic dashboards, custom viz applications, A/B experiments, and algo features • See the problem through a platform lens • Build scalable, flexible data products

• Monitor and support ETL processes moving data from on-premise or hosted servers into Snowflake • Troubleshoot data pipeline issues, including failed loads, data discrepancies, and performance bottlenecks • Design and maintain aggregate and reporting tables optimized for Tableau and Power BI dashboards • Create and maintain Snowflake stored procedures (GET procs) used by APIs to support Angular reporting applications • Optimize Snowflake performance and cost, including warehouse usage, query tuning, and table design • Support ad-hoc data requests from product, engineering, and analytics teams • Integrate third-party vendor data by reviewing technical documentation, designing ingestion processes, and validating data accuracy • Collaborate with application engineers to ensure data structures align with frontend and API requirements • Maintain clear documentation for data pipelines, stored procedures, and integrations

• Design, implement, and maintain complex data pipelines, ensuring scalability and reliability using Airflow, dbt, Rivery, Python, and SQL, enabling robust ingestion and transformation of structured and semi-structured data. • Serve as a strategic partner to business teams, working closely with stakeholders to translate high-level goals into data solutions that support forecasting, performance tracking, and optimization. • Develop and maintain clean, well-documented data models in Snowflake and BigQuery that support analytics, reporting, and operational workflows and contribute to architecture decisions. • Integrate data from a variety of internal and external sources, including Google Analytics and third-party APIs, to support full-funnel visibility across departments. • Enable self-service analytics by ensuring data assets are discoverable and usable via tools such as Tableau, including thoughtful semantic layer design and performance tuning. • Contribute to the development of robust monitoring and observability practices for data quality and pipeline health. • Collaborate on architecture and design decisions, including cloud infrastructure and containerization using AWS, Pulumi, and Docker. • Maintain strong documentation and promote engineering standards that ensure transparency, maintainability, and reusability of data systems.