Software Engineer, Global Advertising Data Platform

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. 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

• 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.