Lead Data Engineer

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Marvik

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2018

🤖 Artificial Intelligence

🤝 B2B

🏢 Enterprise

Artificial Intelligence • B2B • Enterprise

Marvik is a technology consultancy that designs, builds, and deploys production-ready artificial intelligence solutions for enterprise customers. They offer end-to-end AI services including strategy and opportunity discovery, data engineering, model development (agents, LLMs, generative AI, computer vision, predictive analytics), robotics and automation, and on-demand senior AI talent and leadership (fractional CAIO). Marvik focuses on delivering scalable AI that drives business impact across industries like retail, e-commerce, logistics, fintech, manufacturing, healthcare, energy, and government.

📋 Description

• Partner with the CTO and leadership to set the Intelligence strategy and roadmap; own the execution. • Build, hire, and develop the Intelligence team — set the bar for craft, shape the operating cadence, and build the collaboration patterns with product, platform, and engineering. • Stand up the canonical data substrate: schema discipline, tenancy isolation, data contracts, lineage, and governance that AI/ML workloads run cleanly against. • Stand up the ML and AI platform: model lifecycle, feature store, vector store, training and serving infrastructure, and MLOps practice. • Lead the learning and reasoning capabilities of the platform: RAG architectures, agentic data systems, knowledge graphs, and the patterns that let Stratus's data compound into platform intelligence. • Develop and drive evaluation frameworks measuring model quality, agent reliability, drift, and platform effectiveness — make AI workloads observable to engineering, product, and customer success. • Drive the build-vs-buy posture for the AI/ML stack; set production readiness standards for AI workloads in close collaboration with the platform team. • Partner with product on the AI use case portfolio; engage directly with customers when needed to ground Intelligence decisions in real workflow problems.

🎯 Requirements

• 10+ years of professional experience in AI/ML, data engineering, or data science, with 4+ years in formal leadership roles (Senior Manager, Director, or Head of) at a B2B SaaS or AI/ML platform company. • Demonstrated track record of building and leading AI/ML or data teams of 5–15 people, with a strong hiring track record in the AI/ML market within the last two to three years. • Deep technical credibility across the modern AI/ML stack: data platforms (Postgres, pgvector, MongoDB or equivalent), ML platforms (training, serving, MLOps), and generative AI (LLMs, embeddings, RAG, fine-tuning, evals). • Experience shipping production ML and AI workloads to enterprise customers with the trust patterns that come with it: evals, observability, drift detection, confidence scoring. • Excellent communication across all audiences — engineers, product, executives, and customers; strong cross-functional partnership instincts with product, engineering, and customer-facing teams.

🏖️ Benefits

• Projects ranging from massive batch processing to real-time streaming and event-driven architectures. • Exposure to the cutting edge of AI Engineering: integrating Vector Databases and preparing unstructured data (text, images). • Opportunity to work with top-tier open-source orchestration and processing tools (Airflow, Spark, Kafka). • A culture of continuous learning