TECH STACK

• Data: Snowflake, Airflow, Airbyte/Fivetran, dbt, Great Expectations/Soda, Typesense, Python
• Infra/DevOps: AWS, Terraform, GitHub Actions, Datadog
• Analytics: Tableau, Metabase, Amplitude
• AI/Agents: OpenAI, Anthropic, Vertex AI, Langchain, Pydantic AI, RAG, guardrails frameworks

MUST-HAVE QUALIFICATIONS

• 8–12+ years in data engineering/analytics, including 4+ years leading managers and cross-functional teams
• Built/operated high-scale ingestion from APIs/feeds/web data with strong freshness/accuracy SLAs
• Led analytics engineering: metrics definitions, instrumentation, experimentation, exec reporting
• Hands-on with Python and SQL; strong schema/model design; code review competency
• Practical experience shipping LLM/agent systems with measurable business outcomes
• Comfortable with AWS, Airflow, Airbyte/Fivetran, and an AI agent framework (Langchain/Pydantic AI)

PREFERRED QUALIFICATIONS

• Prior success scaling teams in a high-growth startup
• Marketplace/fintech/e-commerce experience (pricing/valuation, risk, payments)
• Familiarity with collectibles or alt-asset marketplaces
• Experience working on operational workflows
  Job Type: Full-time
  Pay: $180,746.02 - $210,618.86 per year

Benefits:

• 401(k)
• 401(k) matching
• Dental insurance
• Health insurance
• Life insurance
• Paid time off
• Vision insurance

Work Location: Remot

ROLE SUMMARY

Hire a hands-on leader to own data pipelines, analytics engineering, and automation/AI agents. Define technical direction and system design; lead multidisciplinary teams to remove growth bottlenecks and scale the platform with AI-driven processes.

CORE RESPONSIBILITIES

• Data Platform & Pipelines: Own ETL/ELT from external marketplaces/partners; enforce freshness/latency/accuracy SLAs; build monitoring, alerting, and incident runbooks; own Alt’s internal asset catalog and expand proprietary datasets.
• Analytics Engineering: Lead the function; deliver a self-servable metrics layer and exec dashboards (GMV, liquidity, funnels, SLAs); own Amplitude taxonomy and experimentation.
• Automation & AI Agents: Identify high-leverage manual workflows (listing QA, price suggestions, dispute/CS routing, KYC review, payout reconciliation) and replace with AI agents; prioritize by ROI with Product/Ops/Risk/Finance.
• Leadership: Grow and manage data engineering, analytics engineering, and automation/ML engineers; manage vendors/budgets; translate goals into a quarterly outcomes roadmap.