Senior Data Architect (Hands on)

GENERAL 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. KEY RESPONSIBILITIES 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. 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. Define the multi-tenant data architecture: tenancy isolation, data residency posture, and per-tenant cost attribution across storage and compute. 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. Modernize legacy data-access patterns via incremental, strangler-fig migrations that keep production stable. 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. Establish data quality, testing, lineage, and observability standards for pipelines and AI/ML serving. Mentor engineers on schema discipline, modern data practices, and AI/ML-readiness patterns. Make canonical decisions that are time-boxed, written, and defensible; hold disagree-and-commit rather than letting schema debate become a standing committee. Use AI-assisted development tools (Claude Code, Copilot, Cursor) as a force multiplier for schema design, query tuning, and migration scripting. Cross-team partnership Partner with database engineering on production data health while owning long-term architectural direction. Partner with ML and application engineering on their data needs — structuring and governing data so it is retrieval-ready and safe to build on. Partner with platform / infrastructure on reliability, disaster recovery, residency, and the multi-tenant operational posture. 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. NICE TO HAVE 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. WHAT SUCCESS LOOKS LIKE — FIRST YEAR The canonical data model is owned and enforced: teams build against stable, documented contracts instead of bespoke forks. Workloads sit in the right stores, legacy anti-patterns are receding, and reliability targets are holding. Tenancy is formalized and per-tenant cost attribution is instrumented, so cost and capacity are observable as we scale. The data substrate is AI-ready — model, contracts, and lineage in place — so AI/ML work builds on a solid foundation rather than waiting on data. You've done it in partnership: the data tier is healthier, and engineers build against your contracts. 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. Apply To This Job