Data Architect
United States · Remote
Learn more about our AI Recruitment solutions.
Axial Search recruits for AI transformation roles across North America, building long-term relationships with leaders across every industry vertical. Apply today to express your interest in this position and others like it. Visit our website to learn more about our process and to find free tools to support your job search — including our live job market dashboard with salary, skills, and hiring-trend data from thousands of AI transformation roles.
What the market looks like
Data architecture has re-emerged as a critical specialty now that most enterprises are building shared feature stores, vector infrastructure, and AI-ready data platforms. The majority of the senior architect postings we see come from technology companies, consulting firms, and financial services. Base compensation for senior data architects generally lands in a $145K–$240K range, and the strongest architects we place are the ones preparing enterprises for AI at scale — not just maintaining yesterday's warehouse.
Job responsibilities
Own the enterprise data architecture — warehouse, lakehouse, streaming, and increasingly AI and vector layers
Set standards and reference patterns for teams building on top of the data platform
Partner with engineering and business leadership on architecture trade-offs and investment
Guide build-versus-buy decisions across data infrastructure and AI-ready tooling
Review designs across data engineering, ML engineering, and analytics teams
Drive evolution toward an AI-ready data foundation — feature stores, vector stores, and semantic layers
Lead cross-team architecture working groups
Mentor senior data engineers and architects across the organization
Candidate requirements
8+ years of data engineering, architecture, or senior technical leadership experience
Deep expertise across warehouse, lakehouse, and streaming patterns
Hands-on experience with modern stacks — Snowflake, Databricks, BigQuery, Kafka, and dbt
Strong fluency with at least one major cloud's data stack (AWS, GCP, or Azure)
Experience designing for AI and ML workloads at scale
Excellent stakeholder and technical communication
