Principal / Staff Data Platform Engineer in ad-tech

DanAds is building an AI-first data foundation where governed events from product and platform systems are transformed into trusted data models, governed data products, analytics, AI capabilities, and customer-facing data experiences.

• 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 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 batch and near-real-time processing;
• Design for high-volume ad-tech workloads;
• 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 optimization 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;
• Deliver the first production data foundation within the first six months;
• Document clear architectural decisions;
• Establish enforceable contracts for events entering the data platform;
• Create and test core canonical entities;
• Make data quality and lineage observable;
• Enable other engineers to contribute without understanding every implementation detail.

• 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 and production experience in Python, Java, Scala, or similar;
• Experience with distributed processing and query technologies such as Spark, Flink, Trino, or comparable platforms;
• Strong understanding of data modeling, partitioning, performance, and storage design;
• Experience building 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;
• Nice to have: Ad-tech or similarly high-volume event-processing experience, multi-tenant SaaS data architecture, semantic layers, data platforms supporting analytics and ML/AI workloads, privacy, residency, and regulated data environments.

• No conditions specified