Responsibilities

Team Introduction The Global Advertising Data Platform team builds the data foundation and products behind advertising across our international products — petabyte-scale ingestion and storage through to the query and data products thousands of internal users and systems depend on every day. The team is dedicated to making an AI-native data platform that enables agents to be reliable colleagues across all data-related scenarios. Semantic modeling, compile-time governance, agent-facing services and tools, evaluation infrastructure, all at global advertising scale. The work is measured by what it delivers: a better advertiser experience, efficiency lift, measurable business growth, or strategy that lands quickly with the platform's help. We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume. Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early. Responsibilities - Design and build the core of an AI-native data platform: Own architecture and hands-on development of high-throughput services serving hundreds of millions of users' advertising activity. You will take up versatile roles and deliver solutions end to end: Identify and define the problem, design technical solutions, build the system with agents, tune and optimize the outcome. - Design agent-facing data services and tool interfaces: Build the APIs and tool surfaces (MCP-style typed interfaces, retrieval over catalog and metadata, structured query execution) through which agents discover and use data — with scoped permissions, typed inputs, and full audit trails, so capability and safety ship together. - Contribute to the semantic layer that makes data agent-consumable: Turn tribal knowledge into machine-readable definitions: metrics, dimensions, lineage, valid join paths, and data contracts, etc. This is the core that decides whether an agent's data work is stable, reliable and verifiable. - Make correctness measurable: Contribute to code and testing standards and to the quality control methods the team runs on — extended to a world where both humans and models write code and generate queries. Build golden query suites, regression and evaluation harnesses, lineage-based impact analysis, and data quality monitoring that catch semantic drift before users do. - Conquer technical challenges: High concurrency, multi-tenant data isolation and governance enforced at query-generation time rather than patched afterwards, system decoupling, cost and performance at scale — these are the recurring challenges of the platform, and you'll work with the team to break through them.

Qualifications

Minimum Qualifications - Individuals who are completing or have recently completed a Bachelor's degree in Computer Science or a related discipline. - Genuine interest in computer science and internet technology, with strong command of at least one programming language — including but not limited to Java, C, C++, Python, or Go. - Solid computer science fundamentals: data structures, algorithms, operating systems, databases and computer networks. - Strong analytical thinking — able to abstract and decompose messy business logic into clean system boundaries. - Comfortable using AI coding agents as part of how you build, together with the judgment to review, test, and take responsibility for what they produce. - Strong drive to learn, and the communication skills to work across engineering, product, and analytics. Preferred Qualifications - Experience with big data or distributed systems technologies such as Spark, Flink, Kafka, Iceberg/Hudi, ClickHouse, Presto/Trino, or similar. - Hands-on experience building applications on large language models — RAG, agent frameworks, tool/function calling, natural-language-to-SQL, or model evaluations. - Familiarity with data warehousing and modeling practice: dimensional modeling, metric/semantic layers, dbt, data lineage, or data catalog and governance tooling. - Open-source contributions, research publications, competitive programming or data competition results, or substantial personal projects. - Prior internship or project experience in advertising technology, recommendation systems, or large-scale data analytics.

Job Information