• Design, build, and maintain scalable data pipelines and architectures • Collaborate with self-organized teams to develop tailor-made technological solutions • Engage in continuous learning and promote a people-first culture

• Designing, building, and maintaining scalable data pipelines and architectures • Collaborating with cross-functional teams to deliver high-quality data solutions

• Define partner onboarding and clean room architecture patterns across Snowflake, LiveRamp, and Databricks that are secure, scalable, and repeatable. • Configure and manage partner-specific clean room environments; deploy and manage Python-based libraries within the platform ecosystem. • Establish and maintain MLOps practices, including model serving, monitoring, and pipeline orchestration for AI/ML features deployed within the platform ecosystem. • Own design and enforcement of granular RBAC policies and least-privilege service accounts. • Serve as the technical lead for onboarding new partners, implementing privacy-preserving controls (e.g., aggregation thresholds and anonymization techniques). • Design, build, and operate scalable ELT pipelines using Snowpark and/or PySpark and advanced SQL to provision Gold datasets. • Implement and evolve identity resolution logic mapping internal data to 3P identifiers (including LUIDs, RampIDs, TransUnion IDs), ensuring privacy-safe practices. • Design and operate scalable data architectures across Snowflake and Databricks supporting batch and near real-time processing patterns. • Build robust automated checks (e.g., Great Expectations or custom SQL assertions) and define quality standards to detect schema drift, null rate spikes, and volume anomalies. • Lead performance optimization across platforms (query tuning, caching, incremental processing) and define and implement query tagging and chargeback models for accurate cost attribution. • Establish monitoring, alerting, runbooks, and standard operating procedures to improve platform reliability and reduce incident time-to-resolution. • Validate that output data adheres to privacy and business requirements, and define test strategies for partner-facing releases. • Serve as the escalation point for diagnosing connection failures, data discrepancies, or latency issues with partner technical teams. • Design and build internal AI agents (using frameworks like LangChain, Snowflake Cortex) and mentor other engineers through code reviews, design discussions, and operational best practices.

• Design, develop, and maintain reliable end-to-end data pipelines (both batch and streaming) that connect internal and external systems in ways that best support marketplace growth, customer experience, and operational efficiency. • Contribute to the performance, scalability, and reliability of our entire data ecosystem. • Work with analysts and other data stakeholders to engineer data structures and orchestrate workflows that encode core business logic. • Implement observability, testing, monitoring, validation, and documentation to ensure accuracy, stability, and consistency throughout the data stack. • Join cross-functional squads and tiger teams to rapidly translate evolving data needs into scalable and extensible data models, metrics, and analytical frameworks. • Mentor data stakeholders throughout the organization, share best practices, and meaningfully contribute to architectural and tooling decisions as the data stack evolves.