Principal Data Engineer

Role overview

Join Sovrn as a Principal Data Engineer for the Data Collective. You will provide architectural ownership and technical leadership across Sovrn’s data platform, while driving AI engineering practices and mentoring other engineers.

Key responsibilities

• Design and evolve data platform systems at scale, including:
  • Data pipelines
  • Data modeling
  • Data lake architecture
• Lead architectural decisions for data infrastructure, including service/data layers and implementation approaches.
• Research, implement, and evolve data processing methods and help democratize data across the organization.
• Establish and champion AI engineering practices:
  • Identify high-leverage opportunities to apply AI
  • Build and operate AI/agentic solutions in production
• Mentor and level up engineers, setting technical standards and best practices.

Requirements

• Strong understanding of real-time streaming and batch pipelines, big data, and data lakes.
• Hands-on distributed data processing experience in the AWS ecosystem.
• Ability to document and communicate architecture clearly across levels.
• Proven ability to lead problem definition, solution design, and implementation plans in ambiguous, fast-moving environments.
• Deep fluency in the programmatic ecosystem, such as OpenRTB, bid request/response flows, auction mechanics, supply path optimization, or similar.
• Strong knowledge of second-layer big data platforms, particularly Snowflake and Databricks (use cases, best practices, implementation, support considerations).
• 10+ years software engineering experience with a strong data engineering/backend track record.
• Demonstrated experience leading AI/agentic engineering in production (shipped, operated, iterated—not only experiments).
• Practical point of view on where AI is and isn’t appropriate.
• 5+ years in adtech data infrastructure (e.g., SSP, DSP, exchange, ad server environments).
• Experience with data security and compliance (e.g., PII, CCPA, GDPR).
• Strong expertise in structured, unstructured, and semi-structured data, plus metadata management, data lineage, and data governance.
• Hands-on experience with LLM integration patterns such as RAG, vector databases, tool use, multi-step agentic workflows, prompt engineering, and evaluation frameworks.
• Understanding of DevOps and SRE practices.

Nice-to-haves

• Not explicitly stated.