Vidmob - Staff Data Engineer
Requirements
• 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. • Modern Data Platform Expertise: • Strong Engineering Foundations: Production-grade SQL and strong programming skills in Python, Scala, Java, or similar languages. Typescript a plus. • Strong Engineering Foundations: • Batch and Streaming Architecture: Experience designing batch, incremental, and near-real-time processing systems at scale. • Batch and Streaming Architecture: • Pipeline and Data Modeling Depth: Experience designing high-volume processing pipelines and the architecture surrounding them. • Pipeline and Data Modeling Depth: • 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 Science Partnership: • Data Quality and Observability: Experience implementing validation, monitoring, lineage, alerting, incident response, and data trust practices for critical pipelines and model-output workflows. • Data Quality and Observability: • AI-First Practitioner: You actively use AI tools to accelerate development, testing, documentation, data discovery, root-cause analysis, and operational workflows. • AI-First Practitioner: • 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. • Pragmatic Technical Leadership: • Direct Communication: Excellent writing and artifact discipline. You can explain architecture, data quality risks, and tradeoffs clearly across technical and non-technical audiences. • Direct Communication: • Industry Background: Familiarity with AdTech, MarTech, platform APIs, ML-powered products, or customer-facing analytics products is a major plus. • Industry Background: • English Fluency: B2+ written and spoken English required. • English Fluency: • Ownership: You own outcomes, not just pipelines. • Ownership: • Please be aware that Vidmob will only contact candidates through emails ending in @vidmob.com. We will never ask for personal information, such as your Social Security number, bank account number, or password, through email. If you receive an email claiming to be from Vidmob that does not come from a @vidmob.com email address, or if the email asks for personal information, please do not respond and report the email to us at [email protected]
Responsibilities
• 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. • Architect Scalable Data Platforms: • Own the Data Lifecycle: Drive ingestion, transformation, modeling, validation, lineage, publishing, and serving across Vidmob’s creative, media, customer, model-output, and performance data. • Own the Data Lifecycle: • Build Reliable Pipelines: Architect batch and near-real-time pipelines that are scalable, observable, replayable, and cost-efficient. • Build Reliable Pipelines: • 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. • Productionize ML and AI Products: • Support LLM and VLM Workflows: Build data foundations for AI-powered products using LLMs, VLMs, multimodal analysis, agent workflows, and reinforcement learning. • Support LLM and VLM Workflows: • Define and Enforce Data Standards: Establish best practices for data contracts, pipeline design, testing, reviews, observability, and production readiness. • Define and Enforce Data Standards: • 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. • Create Trusted Data Products: • Support Platform and Partner Integrations: Build reliable data flows with ad platforms, DSPs, measurement partners, creative systems, customer environments, and internal product surfaces. • Support Platform and Partner Integrations: • Shape Platform Strategy: Influence long-term decisions around tooling, storage, processing frameworks, serving patterns, governance, and cost structure. • Shape Platform Strategy:
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