Principal Data Engineer

About Sovrn

Every interesting company solves important problems for other people. Sovrn is a Software and Data business that helps Open Web businesses be and remain independent. We help them understand their business better, operate more efficiently, and make keep more money.

We believe in the freedom and free-flow of information.

We believe the Open Web is the largest source of this information.

We believe in helping Open Web businesses be and remain Independent.

Through Software products and Data solutions we help our customers:

Understand their business better , so they can make better decisions

Operate their business more efficiently, so they can invest in what matters most

Make (and Keep) more money , so they control their own destiny

About the Role

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

What you'll be doing:

Data Platform Architecture

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

AI Agentic Engineering Leadership

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

Collaboration Mentorship

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

A successful candidate will have:

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 evalua…