Senior Data Architect

Role Description

The Senior Data Architect owns our canonical data architecture — the schema, contracts, tenancy, and governance that every product and every AI/ML workload builds on. You are the single owner of the canonical data model: one normalized definition of the core business objects shared across our products, and the standard the rest of engineering builds against. This is a foundational, hands-on role — you design, prototype, and ship reference implementations and in-repo guardrails, not just diagrams.

Our approach to AI is to build durable, domain-specific data assets rather than commodity model infrastructure: we don't pretrain foundation models and we don't ship thin wrappers around someone else's. The differentiated value lives in how our data is modeled, governed, and made trustworthy for AI — and that is the layer you own.

Qualifications

• 8+ years in data architecture, data engineering, database administration, or analytics engineering, with 3+ years in senior / lead roles.
• Demonstrated ownership of a canonical or enterprise data model / cross-product schema — the model and contracts other teams built against.
• Hands-on MongoDB at production scale (Atlas M40+ ideal): document modeling, aggregation framework, indexing, change streams, sharding, replica sets — and the judgment to recognize the Mongo-as-RDBMS anti-pattern.
• Strong polyglot-persistence judgment: deciding what belongs in documents vs. relational vs. a vector store, and migrating between them incrementally.
• Hands-on relational depth: schema design, indexing strategy, and query tuning, plus familiarity with vector search (Atlas Vector Search, pgvector, or equivalent).
• Production experience making data AI/ML-ready: data architecture supporting RAG, semantic search, embeddings / vector pipelines, or agentic workloads.
• Multi-tenant architecture experience: data residency and per-tenant cost attribution.
• Pipeline / ELT / lake / lakehouse design at scale, with incremental migration strategies that minimize disruption.
• Cloud-native data services (Azure, AWS, or GCP).
• Strong grasp of data quality, testing, lineage, and monitoring — including observability for pipelines and AI/ML serving.
• Comfortable modeling a complex, specialized domain. MEP / AEC / construction experience is a plus; appetite to learn the domain is required.

Requirements

• 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.

Benefits

• Comprehensive and competitive health benefits plan
• Matching 401k contributions
• 20 days annual PTO
• Primarily remote work with occasional annual team onsites
• This is a fully remote position open to candidates based in the United States.