Staff Data Engineer

About the Role

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 mostly remote, but periodic international travel within Latam or to the US will be required.

What you’ll do

• 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.

What we’re looking for

• Senior Technical Experience: 8+ years in data engineering, data platform engineering, or analytics engineering in SaaS, platform, AdTech, MarTech, marketplace, or other data-heavy environments.

• Modern Data Platform Expertise: Deep experience with modern warehouse, lakehouse, orchestration, and transformation technologies including Snowflake, Databricks, and BigQuery , as well as relational and noSQL databases.

• Strong Engineering Foundations: Production-grade SQL and strong programming skills in Python, Scala, Java, or similar languages . Typescript a plus.

• Batch and Streaming Architecture: Experience designing batch, incremental, and near-real-time processing systems at scale.

• Pipeline and Data Modeling Depth: Experience designing high-volume processing pipelines and the architecture surrounding them.

• ML/AI Product Experience: Experience building or supporting production products powered by ML or AI , ideally including LLMs, VLMs, embeddings, recommendation systems, or multimodal data products .

• Data Science Partnership: Strong ability to productionize models, monitor model outputs, build feedback loops, and make experimental work reliable at product scale.

• Data Quality and Observability: Experience implementing validation, monitoring, lineage, alerting, incident response, and data trust practices for critical pipelines and model-output workflows.

• AI-First Practitioner: You actively use AI tools to accelerate development, testing, documentation, data discovery, root-cause analysis, and operational workflows.

• Pragmatic Technical Leadership: You know when to build reusable platform patterns, when to ship tactical fixes, and how to keep one-off work from becoming permanent architecture.

• Direct Communication: Excellent writing and artifact discipline. You can explain architecture, data quality risks, and tradeoffs clearly across technical and non-technical audiences.

• Industry Background: Familiarity with AdTech, MarTech, platform APIs, ML-powered products, or customer-facing analytics products is a major plus.

• English Fluency: B2+ written and spoken English required.

• Ownership: You own outcomes, not just pipelines.