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.