Principal Data Engineer

Sovrn•Boulder, CO

7h•Hybrid

About The Position

Sovrn is a supply-side platform (SSP) built to help publishers grow, protect, and understand their businesses. We operate at the intersection of programmatic advertising infrastructure and publisher monetization — processing hundreds of billions of ad requests daily across a global, high-throughput exchange. We’re looking for a Principal Software Engineer (Data) with deep roots in adtech data infrastructure and a genuine conviction about what AI-native data engineering looks like in practice. This is a specialized principal-level engineering role — one that carries all the architectural ownership and technical leadership expectations of a Principal Software Engineer, focused on Sovrn’s Data Collective. From a generative/agentic AI capabilities standpoint, we already use LLMs and agentic tooling across our data stack and, we’re looking for is someone who can help us take that from general adoption to intentional practice — who has strong opinions about where AI creates real leverage in a high-throughput adtech environment, and who can bring the rest of the engineering organization along with them. Languages/components/tools in our stack: Python, Pyspark, Kafka, Databricks, AWS

Requirements

10+ years of software engineering experience, with a strong data engineering and backend track record
5+ years working specifically in adtech data infrastructure, SSP, DSP, exchange, or ad server environments
Deep fluency in the programmatic ecosystem: OpenRTB, bid request/response flows, auction mechanics, supply path optimization, or similar
Excellent understanding of real-time streaming and batch pipelines, big data, and data lakes; hands-on experience in distributed data processing in the AWS ecosystem
Strong understanding of second-layer big data platforms such as Snowflake and Databricks, applicable use cases, best practices, implementation, and support considerations
Strong experience in structured, unstructured, and semi-structured data techniques; metadata management, data lineage, and data governance
Experience with data security and compliance (PII, CCPA, GDPR, etc.)
Demonstrated experience leading AI or agentic engineering efforts in production environments; not just experimentation, but shipped, operated, and iterated on
Hands-on experience with LLM integration patterns: RAG, vector DBs, tool use, multi-step agentic workflows, prompt engineering, and evaluation frameworks
Ability to clearly document and communicate architectural concepts at multiple levels; ability to lead problem definition, solution designs, and implementation work plans
Understanding of DevOps and SRE practices
Comfort driving technical decisions in ambiguous, fast-moving environments
A point of view on where AI is and isn’t the right too, and the credibility to make that case

Responsibilities

Own the design and evolution of data platform systems that operate at exchange scale; high throughput, real-time streaming, and always-on batch pipelines
Lead architectural decisions across data infrastructure: pipeline design, data modeling, lakehouse architecture, and data services layers
Specify data platform components and configurations required for pipeline implementation; define pipeline observability to understand and improve performance at massive scale
Research, implement, and evolve methods to process and democratize data across the organization
Drive technical standards, design reviews, and engineering best practices across a senior team
Partner with product, data science, and platform teams to ship end-to-end
Establish and champion AI engineering practices across the team, from prompt engineering and RAG patterns to agentic workflow design, LLM evaluation, and progressive implementation of agentic design patterns
Identify high-leverage opportunities to apply AI in our data stack: intelligent pipeline optimization, anomaly detection, automated data quality, forecasting, and LLM-powered data services
Lead the evolution of our existing LLM and agentic tooling from passive use to intentional, well-architected integration within our data platform
Set standards for how we evaluate, trust, and operate AI-powered systems in production, including observability, fallback behavior, and model governance
Help the broader engineering team build fluency and confidence with AI tooling, not just tolerance of it
Provide domain expertise across the organization to enable business growth through data services and data models
Provide counsel to all consumers and stakeholders of data to enable efficient and impactful use of our data assets
Mentor and level up engineers through code review, design collaboration, and hands-on guidance; foster a culture of innovation and continuous learning
Operate with high autonomy across ambiguous, high-impact problems