We are seeking an experienced Staff Data Engineer to architect, scale, and operationalize the data systems that power Vidmob’s creative intelligence platform
Vidmob turns creative into a measurable growth driver by connecting creative assets, model outputs, platform metadata, media delivery, and business outcomes. That requires scalable, reliable data platforms for analytics, machine learning, customer-facing reporting, APIs, partner integrations, and AI-driven workflows
This role is especially important as Vidmob builds more products driven by ML and AI, including LLMs, VLMs, creative scoring systems, and agentic workflows. We need a data engineering leader who can partner deeply with Data Science to productionize models, serve model outputs reliably, and turn experimental intelligence into durable customer-facing products
Because AI is changing how data systems are built and consumed, you need to be an AI-forward practitioner. You will use AI to accelerate engineering, validation, observability, root-cause analysis, documentation, and data discovery
Our global team works in English, so strong written and spoken English skills are essential. This position is most remote, but periodic international travel within Latam or to the US will be required
Architect Scalable Data Platforms: Lead the design and development of data systems that support analytics, ML/AI products, reporting, APIs, integrations, and customer-facing data products
Own the Data Lifecycle: Drive ingestion, transformation, modeling, validation, lineage, publishing, and serving across Vidmob’s creative, media, customer, model-output, and performance data
Build Reliable Pipelines: Architect batch and near-real-time pipelines that are scalable, observable, replayable, and cost-efficient
Productionize ML and AI Products: Partner with Data Science to turn models, scores, embeddings, prompts, evaluations, and experimental outputs into reliable production data products and customer-facing capabilities
Support LLM and VLM Workflows: Build data foundations for AI-powered products using LLMs, VLMs, multimodal analysis, agent workflows, and reinforcement learning
Define and Enforce Data Standards: Establish best practices for data contracts, pipeline design, testing, reviews, observability, and production readiness
Create Trusted Data Products: Build governed datasets and serving patterns that support dashboards, APIs, exports, partner integrations, ML workflows, benchmarks, and agent-ready use cases
Support Platform and Partner Integrations: Build reliable data flows with ad platforms, DSPs, measurement partners, creative systems, customer environments, and internal product surfaces
Shape Platform Strategy: Influence long-term decisions around tooling, storage, processing frameworks, serving patterns, governance, and cost structure