Data Architect - Market

Job Description: Architect and own the AI context platform Design end-to-end platform architecture: ingestion ? parsing/chunking ? enrichment ? embeddings ? vector indexing ? retrieval/serving Define scalable patterns for incremental refresh, backfills, re-embeddings, deduplication, and lineage across unstructured sources Set technical direction for retrieval quality (query strategies, hybrid search, metadata filtering, reranking) in partnership with AI engineers Evaluate and select infrastructure, tooling, and cloud services to support platform needs across AWS/Azure/GCP environments Design and deliver semantic and governed data products Architect and implement semantic layers (metrics/entities) that power BI and agent reasoning consistently across the platform Define data contracts and context contracts for AI inputs (schemas, metadata requirements, freshness, citation expectations) Establish standards for discoverability, documentation, and reusability across datasets and indexes Own the dbt or semantic layer tooling strategy and ensure consistent application across workstreams Own reliability and performance at the platform level: monitoring, alerting, SLAs/SLOs, runbooks, incident response, and postmortems Drive cost and latency optimization across Snowflake, lakehouse, and vector infrastructure Set engineering standards for CI/CD, testing, and evaluation (retrieval eval sets, regression tests, online telemetry) Implement security-by-design: RBAC/ABAC patterns, PII redaction, retention controls, audit logging, and safe access pathways for agent tools Partner with Security/Legal/Compliance to define and enforce guardrails for AI access to enterprise knowledge Own governance patterns for sensitive data handling across the platform Facilitate architectural decisions across teams and functions, building alignment without direct authority Set best practices and mentor engineers via design reviews, code reviews, and documentation Requirements: 8–12+ years in data engineering, data architecture, or platform roles with significant hands-on delivery Expert SQL and strong Python (or Scala/Java); deep production engineering habits Hands-on Snowflake expertise including advanced data modeling, pipeline design, performance tuning, and operating at scale in production Proven experience designing cloud data architectures on AWS, Azure, or GCP — including storage, compute, orchestration, and networking considerations Hands-on experience with vector search and embeddings (pgvector /Pinecone/ Weaviate /OpenSearch/Elastic) and retrieval patterns (semantic retrieval, hybrid search, reranking) Experience with dbt or comparable semantic layer tooling in a production environment Demonstrated ability to lead cross-functional technical initiatives and drive alignment across teams Strong written and verbal communication skills — able to present architecture decisions to both technical and non-technical audiences. Benefits: medical, dental and vision coverage annual incentive compensation program 401(k) plan with generous employer match employee stock purchase plan generous Paid Time Off policy paid parental leave adoption assistance wellness programs