Stratus

Senior Data Architect (Hands on)

Remote (United States) · Senior · Contract

Sponsorship not specifiedDetected 17 days ago

MongoDBSnowflakeDatabricksVector DatabasesAWSGCPAzureRESTMachine LearningData EngineeringRAGComplianceLeadership

About the role

AI/ML readiness Architect the data layer so AI/ML workloads - vector search, embeddings pipelines, RAG-grounded retrieval, model training - run on a clean, governed substrate.
Make production data AI-ready: well-modeled, contract-enforced, lineage-tracked, and drift-detectable.
Define the multi-tenant data architecture: tenancy isolation, data residency posture, and per-tenant cost attribution across storage and compute.

Design the data-side integration patterns these workloads depend on, such as feature-store and vector-store patterns across document, relational, and embedding data.
Data architecture Own the canonical data model - the normalized definition of the core business objects shared across our products - and decide what is canonical versus tenant-specific.
Establish data architecture standards, data contracts, and schema discipline the rest of engineering builds against, enforced in-repo.
Exercise strong polyglot-persistence judgment: what belongs in document vs. relational vs. vector stores, and how to migrate between them without big-bang rewrites.
Modernization Lead staged modernization toward the right mix of stores and patterns for transactional, analytical, and AI/ML use cases - improving scalability, governance, and usability while minimizing disruption.
Own the architectural direction of the data pipeline and lake / lakehouse layer: ingestion, transformation, orchestration, and storage tiers.
Lead the move from homegrown pipelines to proven, industry-standard platforms, balancing build-vs-buy and total cost of ownership.
Technical leadership Drive hands-on prototypes, reference implementations, and in-repo guardrails.
Define the data, storage, and retrieval patterns the rest of engineering builds against.
Use AI-assisted development tools (Claude Code, Copilot, Cursor) as a force multiplier for schema design, query tuning, and migration scripting.

Knowledge-graph, ontology, or semantic-layer experience.
CDC and cross-engine sync (MongoDB Change Streams, Debezium, or equivalent).
Lakehouse platforms (Databricks, Snowflake, or open table formats - Iceberg, Delta, Hudi) and feature stores (Feast or equivalent).
Data governance for AI/agent access to production data: query-cost controls, read-path safety, lineage, and audit for higher-risk use cases.
SOC 2 and data-classification experience.
Azure data ecosystem (Data Factory, Synapse, Functions, Event Grid).
MongoDB certification (Associate DBA / Developer or higher) or substantive MongoDB University coursework.
Workloads sit in the right stores, legacy anti-patterns are receding, and reliability targets are holding.

Cross-team partnership Partner with database engineering on production data health while owning long-term architectural direction.

This listing is sourced directly from Stratus's careers page and normalized into a canonical job model.