[Hiring] Senior Data Engineer @pubx

Role Description

PubX builds next-generation agentic advertising infrastructure. Our AI makes real-time, revenue-critical decisions for digital publishers and advertisers. Our Bid Intelligence uses machine learning to optimize every programmatic ad auction individually, generating measurable revenue uplift for publishers.

The problem we’re solving is that digital publishers and advertisers leave significant revenue on the table because ad sales are still largely manual, static, or rule-based. Most ad campaign management systems lack the intelligence to evaluate context, demand, and deal potential in real time.

As a founding member of AgenticAdvertising.org, we're building the next generation of autonomous advertising infrastructure.

What You'll Work On

• Tech: Python, SQL, Spark, Airflow, dbt, Kafka/Kinesis/SQS, AWS, Terraform/CDK, modern ETL
• Design and maintain high-volume data pipelines (batch + streaming) powering agentic AI features and core product workflows
• Build event-driven components using Kafka and message queues, including idempotency patterns, replay strategies, and backfill mechanisms
• Develop data models and transformation layers (lakehouse patterns, dbt-style modeling) supporting both analytics and ML/AI consumption
• Own data quality and reliability: schema management, validation, lineage, SLAs, and incident response
• Enable AI/ML workflows with robust datasets for training, evaluation, feature generation, and feedback loops from production agents
• Deploy and operate data infrastructure on AWS using infrastructure-as-code

Qualifications

• Strong data engineering fundamentals: data modeling, partitioning, performance tuning, and cost-aware design for high-volume workloads
• Experience building streaming and event-driven systems (Kafka/queues), including handling late/out-of-order events, backfills, and real-world data edge cases
• Strong SQL + Python skills, and comfort with modern data stack tooling (e.g., Spark, Airflow/Dagster, dbt, warehouse/lakehouse patterns)
• Hands-on AWS experience with production operations for data systems: monitoring, incident response, and security considerations (PII, access control, encryption, auditability)
• Familiarity integrating data with AI/ML and agentic systems: feature pipelines, evaluation datasets, grounding/citations inputs, and feedback capture from agent outcomes

Who This Role Will Suit

• Engineers who like a mix of autonomy and collaboration, and who are comfortable working in an evolving environment
• Comfort with async collaboration and clear written communication is important, especially in a distributed team
• Experience with agentic coding tools (e.g. Cursor and Claude Code) to plan, scaffold, refactor, and debug production code

Bonus (not required)

• Experience with AdTech or other high volume real-time systems

Benefits

• Competitive salary with meaningful equity
• Fully remote, async-friendly working
• Supportive, low-ego engineering culture
• Budget for learning and professional development

Interview Process

• CV & Profile Review – Relevant experience and background
• Initial Chat (30 mins) – Motivation and role fit
• Technical Interview (60 mins) – Architecture, design choices, and real scenarios
• Practical Exercise + Discussion (60 mins) – A small task related to the role