• Develop efficient and scalable data extraction methodologies to retrieve data from diverse sources, such as databases, APIs, web scraping, flat files, and streaming platforms. • Design and implement robust data loading processes to efficiently ingest and integrate data into the latest data warehousing technology, ensuring data quality and consistency. • Develop and maintain staging processes to facilitate the organization and transformation of raw data into structured formats, preparing it for downstream analysis and reporting. • Implement data quality checks and validation processes to identify and address data anomalies, inconsistencies, and integrity issues. • Identify and resolve performance bottlenecks in data extraction and loading processes, optimizing overall system performance and data availability. • Ensure adherence to data security and privacy standards throughout the data extraction and warehousing processes, implementing appropriate access controls and encryption mechanisms. • Create and maintain comprehensive documentation of data extraction and warehousing processes, including data flow diagrams, data dictionaries, and process workflows. • Mentor and support junior data engineers, providing guidance on best practices, technical design, and professional development to elevate overall team capability and performance. • Collaborate with cross-functional teams, including data scientists, data analysts, software engineers, and business stakeholders, to understand their data requirements and provide efficient data engineering solutions. • Stay updated with the latest advancements in data engineering, data warehousing, and cloud technologies, and proactively propose innovative solutions to enhance data extraction and warehousing capabilities.

• 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.