AI Data Engineer
Listed on 2026-06-18
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IT/Tech
Data Analyst, Data Engineering, Data Science Manager, AI Engineer (Applied/Software)
Let’s face it, a company whose mission is human transformation better have some fresh thinking about the employer/employee relationship.
We do. We can’t cram it all in here, but you’ll start noticing it from the first interview.
Even our candidate experience is different. And when you get an offer from us (and accept it), you get way more than a paycheck. You get a personal Better Up Coach, a development plan, a trained and coached manager, the most amazing team you’ve ever met, and most importantly, work that matters.
Join us and we promise you the most intense and fulfilling years of your career, doing life-changing work in a fun, inventive, soulful culture.
If that sounds exciting—and the job description below feels like a fit—we really should start talking.
We are a hybrid company with a focus on in-person collaboration when necessary. Employees are expected to be available to work from one of our office hubs at least two days per week, or eight days per month. Our US hub locations include:
Austin, TX;
New York City, NY;
San Francisco, CA; and the Washington, DC metro area. Please ensure you can realistically commit to this structure before applying.
Better Up’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.
- 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 Rev Ops 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.
- 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, Big Query, 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, Hub Spot, 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…
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