Principal / Staff Data Platform Engineer

Start: ASAP / Flexible

Contract length: 6 months + prolongations

Contract type: B2B

Remote: 100% remote

Business trips: none

The Principal / Staff Data Platform Engineer will lead the technical development of a new AI-first data foundation, focused on transforming governed events into trusted data products and analytics.

Main Responsibilities:

• Lead the technical design of the new data platform.

• Define data movement from the Iceberg-based event layer to analytical and operational data products.

• Evaluate and select technologies for data querying, transformation, orchestration, storage, and serving.

• Design canonical entities and reusable data models across various business domains.

• Establish scalable patterns for batch and near-real-time data processing.

• Make architectural decisions and establish engineering standards for the platform.

• Build data quality, observability, lineage, and reconciliation into the platform.

• Define engineering standards for testing, deployments, versioning, and schema evolution.

• Mentor other data engineers as the team grows.

Key Requirements:

• Significant experience in designing and operating production data platforms.

• Strong experience with distributed data systems and high-volume event data.

• Deep understanding of lakehouse architectures and open table formats like Apache Iceberg.

• Strong SQL knowledge and experience in Python, Java, or Scala.

• Experience with distributed processing technologies like Spark or Flink.

• Strong understanding of data modelling, performance, and storage design.

• Experience in building both batch and near-real-time data pipelines.

• Experience with cloud infrastructure, preferably AWS.

• Strong understanding of CI/CD and infrastructure-as-code principles.

• Experience with data contracts and schema evolution.

Nice to Have:

• Experience in ad-tech or high-volume event processing.

• Multi-tenant SaaS data architecture knowledge.

• Experience with semantic layers.

• Experience building data platforms for analytics and ML/AI workloads.

• Knowledge in privacy, residency, and regulated data environments.