Data Science / Analytics Engineering Staff (Data Acceleration)

Quick Facts

• Role: Staff-level Data Acceleration (Data Science / Analytics Engineering)

Description

You will build AI agents and internal systems that accelerate end-to-end data analysis workflows—forming hypotheses, writing and running queries, interpreting results, and drafting recommendations with evaluation, review, and guardrails. The role includes building retrieval infrastructure and evaluation loops so AI systems can query the warehouse reliably, automating the data lifecycle with self-healing pipelines and data quality agents, and generating ship/no-ship guidance from A/B tests. You will own the full lifecycle from prototyping with LLMs through production shipping and ongoing quality monitoring.

Responsibilities

• Build AI agents that can safely explore data, run queries, interpret results, and generate actionable recommendations with human review loops

• Build retrieval infrastructure and evaluation loops for accurate warehouse querying using semantic context and metadata

• Convert AI-assisted workflows into scalable systems, reusable tools, and team-wide patterns

• Automate data lifecycle activities (self-healing pipelines, automated dbt model generation/validation, data quality and diagnosis workflows)

• Ship AI-powered A/B test analysis agents (statistical issue detection, likely driver identification, ship/no-ship recommendations)

• Build internal data products and self-serve AI interfaces to replace ad hoc requests

• Prototype with LLMs, evaluate accuracy, design UX, ship to production, and monitor quality over time

Requirements

• 6+ years in data science, analytics engineering, data engineering, or a related role

• Deep SQL and analytics judgment across metrics, experiments, and data models

• Strong product sense and stakeholder-focused workflow design

• Hands-on LLM experience (frontier models, agents, RAG systems, evals, or AI-powered workflows)

• Pipeline and modeling fluency with dbt, warehouse schemas, and data quality issues

• Builder mentality with fast shipping, quality measurement, and iteration

• Autonomy to define the roadmap for a new function

Benefits

• Set the standard for modern, AI-native data team workflows

• High leverage: systems that multiply output across the data team and stakeholders

• Direct impact with rapid idea-to-shipped-system timelines

• Access to frontier infrastructure and expert guidance for building AI systems