Summary
✨ AI‑Generated
Lead the architecture and first production implementation of a new AI-first data platform. You will define how immutable events flow into canonical models, governed data products, analytics, AI capabilities, and semantic layers. This is a high-impact technical leadership role focused on establishing engineering standards and a scalable data foundation.
Highlights
Build a new AI-first data foundation from the ground up and make key architectural decisions. The role offers substantial technical ownership, influence over engineering standards, and the opportunity to establish a production-grade platform supporting analytics and AI.
Description
DanAds is building a new AI-first data foundation from the ground up.
Product and platform systems will emit governed events into an open data layer, and the Data & AI team will own what happens downstream: transforming those events into trusted canonical models, governed data products, analytics, AI capabilities and eventually customer-facing data experiences.
We are looking for a Principal / Staff Data Platform Engineer to be the technical lead for this foundation.
This is not a role maintaining an existing warehouse.
You will help make the architectural decisions, establish the engineering standards and build the first production version of the platform.
The target architecture is designed around immutable event data, canonical models, governed contracts and a semantic layer that serves both humans and AI agents.
What you'll do
Lead the technical design of DanAds' new data platform.Define how data moves from the Iceberg-based event layer into downstream analytical and operational data products.Evaluate and select the appropriate technologies for querying, transformation, orchestration, storage and serving.Design canonical entities and reusable data models across advertising, inventory, campaigns, orders, billing and customers.Establish scalable patterns for both batch and near-real-time processing.Design for high-volume ad-tech workloads from the beginning.Build data-quality, observability, lineage and reconciliation into the platform.Work with Platform Engineering on CI-enforced data contracts between source systems and Data & AI.
Establish tenant isolation, access controls and regional data boundaries.Define engineering standards for testing, deployments, versioning and schema evolution.Own performance and cost optimisation of the data platform.Mentor other data engineers as the team grows.Act as a senior technical partner to the Head of Data & AI and Platform Engineering.
What we're looking for
Significant experience designing and operating production data platforms.Strong experience with distributed data systems and high-volume event data.Deep understanding of modern lakehouse architectures and open table formats such as Apache Iceberg.Strong SQL plus production experience in Python, Java, Scala or similar.Experience with distributed processing/query technologies such as Spark, Flink, Trino or comparable platforms.
Strong understanding of data modelling, partitioning, performance and storage design.Experience building both batch and near-real-time data pipelines.Experience with cloud infrastructure, preferably AWS.Strong understanding of CI/CD, infrastructure-as-code and production observability.Experience with data contracts, schema evolution and data-quality frameworks.Ability to make architectural decisions without over-engineering the first version.
Particularly valuable
Ad-tech or similarly high-volume event-processing experience.Multi-tenant SaaS data architecture.Experience with semantic layers.Experience building data platforms that support both analytics and ML/AI workloads.Experience with privacy, residency and regulated data environments.
What success looks like
Within your first six months:
The first production data foundation is running.Clear architectural decisions have been made and documented.Events entering the data platform have enforceable contracts.Core canonical entities exist and are tested.Data quality and lineage are observable.Other engineers can contribute without needing to understand every implementation detail.
