About Appier

Appier (TSE: 4180) is an AI-native Agentic AI as a Service (AaaS) company that empowers businesses to create value through cutting-edge AdTech and MarTech solutions. Founded in 2012 with the vision of “Making AI Easy by Making Software Intelligent,” Appier helps businesses turn AI into ROI through its Ad Cloud, Personalization Cloud, and Data Cloud—each powered by Agentic AI that enables autonomous, adaptive, and real-time decision-making. Today, Appier operates 17 offices across APAC, the US, and EMEA, and is listed on the Tokyo Stock Exchange. Learn more at www.appier.com.

Our Mission

The Data Engineering team's mission is to provide the highest-quality data so that our teams, products, and systems can make the best possible decisions — and to ensure that data meets customer, company, and industry standards for consumption. In practice, the data we serve should be reliable, discoverable, and continually expanding to cover the core business concepts our products depend on.

About the Role

The Data Engineering team owns the data pipelines and serving APIs behind Ad Cloud — the layer that turns raw ad-serving events into high-quality, reliable, and queryable data for internal teams, products, and external customers.

We are looking for a Product Manager (or Technical PM) who is comfortable in both the technical and the domain dimensions of this work — someone who can read the code and the data model, reason about a pipeline or an API contract, and understand what a data-quality control, an attribution correction, or a cost reconciliation actually means to the business. You will own a well-scoped set of data products first — the customer data platform, the data query/serving APIs, and the data-quality and reconciliation layers built on top of them — and grow into larger, multi-system ownership over time.

What You Will Do

Product Design & Delivery

Own the full lifecycle of well-defined data products — the customer data platform, the data query/serving APIs (including agent-facing interfaces), and the data-quality, control, and reconciliation layers built on top of them.
Write clear PRDs and design docs covering user flows, key states, edge cases, and acceptance criteria.
Contribute to system-architecture mapping and API contracts, with guidance from senior leadership on the largest decompositions.
Balance feature scope against release timelines and data-reliability commitments.

Specification & Execution

Write functional specs engineers can build from — request/response shapes, error handling, and state-transition behavior for owned workflows; API-level details worked out together with engineering.
Break initiatives into staged, dependency-aware milestones across large-scale batch/streaming data pipelines and the serving layer.
Work directly with engineering on implementation trade-offs and resolve product-scope decisions; escalate architectural concerns appropriately.

Domain & Data Quality Ownership

Build a working understanding of the domain — audience data, ad-serving events, attribution, delivery controls (e.g. frequency and spend capping), and cost/performance reconciliation — and how they are exposed to consumers through query tooling and APIs.
Treat data quality, reliability, integrity, accessibility, and understandability as first-class product outcomes; define the metrics and guardrails (SLO, freshness, observability) that hold them.
Represent the team's data to downstream consumers (internal teams, partners, external customers) and translate their needs back into pipeline and API requirements.

Agentic AI in the Product Workflow

Design, prototype, and help ship agentic AI workflows and features where they fit the team's products; rapidly turn concepts into working prototypes using AI-assisted tooling.
Use AI/LLM tools daily as part of how you spec, investigate data, and orchestrate delivery.

Cross-Functional Collaboration

Translate business requirements into specs for your domain and drive decisions within scope.
Support phased rollouts with feature gating and pre-release alignment for any customer-visible data change.