Lead Data Engineer

Marvik

Lead Data Engineer responsible for developing AI-driven data pipelines and infrastructure. Collaborate with the CTO and build a team within a fully remote environment.

Posted 7/4/2026full-timeRemote • 💃 Anywhere in Latin AmericaSeniorWebsite

Tech Stack

Tools & technologies

MongoDBPostgres

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

AI/MLData EngineeringData ScienceModel Lifecycle ManagementFeature Store DevelopmentGenerative AIDrift DetectionConfidence ScoringSchema DisciplineData Governance

Soft Skills

Excellent CommunicationCross-Functional Partnership