• Design, implement, and maintain scalable data pipelines to collect, process, and store data from various sources. • Ensure data quality, accuracy, and consistency throughout the pipeline. • Design and implement existing data models for predictive analytics, machine learning, and data exploration. • Optimize data structures and storage to support predictive analytics/machine learning processes. • Work closely with cross-functional teams to integrate data from diverse sources, including databases, APIs, and external data providers. • Develop and maintain ETL processes to transform and enrich raw data into actionable insights. • Monitor and optimize the performance of data pipelines and databases to meet business requirements. • Stay up-to-date with the latest advancements in data engineering and data science technologies. • Share knowledge and mentor junior team members.

• Design, develop, and maintain scalable ETL (Extract, Transform, Load) pipelines to process large volumes of data from diverse sources. • Build and optimize data storage solutions, such as data lakes and data warehouses, to ensure efficient data retrieval and processing. • Integrate structured and unstructured data from various internal and external systems to create a unified view for analysis. • Ensure data accuracy, consistency, and completeness through rigorous validation, cleansing, and transformation processes. • Maintain comprehensive documentation for data processes, tools, and systems while promoting best practices for efficient workflows. • Collaborate with product managers, and other stakeholders to gather requirements and translate them into technical solutions. • Participate in requirement analysis sessions to understand business needs and user requirements. • Provide technical insights and recommendations during the requirements-gathering process. • Participate in Agile development processes, including sprint planning, daily stand-ups, and sprint reviews. • Work closely with Agile teams to deliver software solutions on time and within scope. • Adapt to changing priorities and requirements in a fast-paced Agile environment. • Conduct thorough testing and debugging to ensure the reliability, security, and performance of applications. • Write unit tests and validate the functionality of developed features and individual elements. • Writing integration tests to ensure different elements within a given application function as intended and meet desired requirements. • Identify and resolve software defects, code smells, and performance bottlenecks. • Stay updated with the latest technologies and trends in full-stack development. • Propose innovative solutions to improve the performance, security, scalability, and maintainability of applications. • Continuously seek opportunities to optimize and refactor existing codebase for better efficiency. • Stay up to date with cloud platforms such as AWS, Azure, or Google Cloud Platform. • Collaborate effectively with cross-functional teams, including testers, and product managers. • Foster a collaborative and inclusive work environment where ideas are shared and valued.

1. Responsible for data cleaning (ETL) and data warehouse construction to support large-scale AI models. 2. Responsible for training and fine-tuning large AI models to meet the requirements of specific business scenarios. 3. Responsible for developing supporting tools, such as dashboards and general business logic, to ensure the practicality of AI model applications. 4. Must have hands-on development experience and be able to lead a team or independently complete projects related to data collection and development.

• 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