AI Data Engineer

BetterUp•Austin, TX

3d•Hybrid

About The Position

BetterUp’s GTM Data & Operations team is investing in the infrastructure, intelligence, and automation that powers our go-to-market engine. As an AI / Data Engineer (P3), you will sit at the intersection of data engineering and applied AI—building the pipelines, models, and systems that help our Sales, Marketing, and Customer Success teams move faster, make smarter decisions, and drive measurable business impact. This is a high-impact role for someone who thrives when technology and strategy meet.

Requirements

3–5 years of experience in data engineering, analytics engineering, GTM engineering or applied AI roles.
Strong proficiency in Python and SQL.
Experience building and maintaining data pipelines, reverse ETL workflows and working with modern cloud data warehouses (Snowflake, BigQuery, or Redshift).
Familiarity with agentic coding tools (Claude Code, Cursor, etc.).
Familiarity with GTM tools and ecosystems (Salesforce, Hubspot, Clay, etc.).
Demonstrated ability to build and deploy models (lead scoring, forecasting, classification) in a business context.
Experience integrating data and AI into CRM and/or marketing automation platforms (Salesforce, HubSpot, Marketo, etc.).
Familiarity with BI tools such as Looker or Hex for dashboard development and self-serve analytics.
Familiarity with software engineering principles and tools (version control, CI/CD, testing, code review, Github etc.)
Systems-level thinking with the ability to trace data and logic across multiple platforms (e.g., HubSpot → Salesforce → Snowflake → BI layer) and understand how changes in one system ripple across the stack.
Strong communication skills with the ability to translate complex technical work into business impact.
Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field—or equivalent practical experience.

Nice To Haves

Prior experience in a Revenue Operations or GTM Analytics function at a B2B SaaS company.
Hands-on experience building with LLMs or deploying generative AI solutions in a production environment.
Hands-on experience with decoupled or "headless" semantic layers (Cube, Snowflake Cortex etc.)
Track record of working in a high-growth startup or scale-up environment where priorities evolve quickly.
Hands-on experience with dbt for data transformation.

Responsibilities

Design, build, and maintain scalable data pipelines that serve clean, reliable data across the GTM stack.
Own and evolve our data warehouse architecture (Snowflake) to support GTM analytics, reporting, and AI workloads.
Establish and enforce data quality standards, documentation practices, and governance frameworks to ensure trustworthy data at scale.
Develop and maintain dbt models, SQL transformations, and Python scripts that power self-serve reporting and downstream analysis.
Define and track key GTM metrics, ensuring consistent definitions and reliable data pipelines that support executive and board-level reporting.
Develop and deploy AI tools for high-impact GTM use cases including lead scoring, pipeline forecasting, churn prediction, and customer segmentation.
Partner with Sales, Marketing, and RevOps stakeholders to identify where AI can automate workflows, surface insights, and unlock new capacity.
Integrate LLMs and generative AI tooling into GTM systems to streamline operations and accelerate decision-making.
Measure and iterate on created systems, translating outputs into actionable tools that are accessible to non-technical stakeholders.
Lead cross-functional efforts to integrate AI into core GTM workflows, ensuring adoption across direct and neighboring teams.
Coach teammates through AI adoption, helping them build new capabilities and overcome resistance to change.
Lead cross-functional efforts to integrate AI into core GTM workflows, ensuring adoption across direct and neighboring teams.
Set measurable AI improvement objectives based on functional strategy with minimal manager direction.
Ensure failed AI experiments generate documented learnings that inform future decisions and prevent rework.
Make decisions about AI processes and tools that impact your direct team, neighboring teams, and cross-functional partners.
Re-architect team processes to incorporate AI at scale—automated reporting, intelligent workflow routing, systems building.