Senior Analytics Engineer

BetterUp•Austin, TX

2d•$158,000 - $220,000•Hybrid

About The Position

We are looking for a Senior Analytics Engineer with experience building data modeling systems within a modern data stack, supported by intelligent data warehousing and adaptable business intelligence systems, to join our team. At BetterUp we believe data is critical to our success and this role on the Analytics Engineering team will be crucial to this strategy of delivering exceptional data products that will drive key business decisions. If you are passionate about helping people reach their potential, thrive in a fast-paced startup environment where you get to build the future, and love data then this is the role for you. Our team thrives at the intersection of human expertise and AI capability. As an AI-forward company, adaptation and continuous learning are part of our daily work. We're looking for teammates who are excited to evolve alongside technology – people who experiment boldly, share their discoveries openly, and help define best practices for AI-augmented work. These professionals thoughtfully integrate AI into their work to deliver exceptional results while maintaining the human judgment and creativity that drives real innovation. During our interview process, you’ll have opportunities to showcase how you harness AI to learn, iterate, and amplify your impact. As a Senior Analytics Engineer, you’ll: Drive functional strategy for AI adoption, identifying transformational opportunities that create competitive advantage in how we build and scale our data products. Build scalable AI-powered systems that fundamentally change how analytical work gets done across multiple teams. Serve as a trusted advisor to leadership on AI capabilities, limitations, and strategic implications for our data platform. Establish governance frameworks and best practices for responsible AI usage at scale within the analytics function. Transform core data and analytics processes through strategic AI integration—including intelligent pipeline monitoring, automated data quality checks, and AI-assisted insight generation. Quantify AI impact on key data metrics and make data-driven recommendations for continued investment. During our interview process, you’ll have opportunities to showcase how you harness AI to learn, iterate, and amplify your impact. We encourage all candidates to share examples of how they’ve used AI tools in their work—whether it’s AI-assisted SQL generation, LLM-powered data documentation, automated anomaly detection, or other applications. We view AI fluency as a complement to craft, not a replacement for it.

Requirements

Minimum 5+ years of relevant experience in data engineering and/or analytics engineering
Advanced SQL skills with 5+ years of deep experience with Snowflake or similar DB
Expertise with data modeling and ETL/ELT (DBT preferred, pipeline development a plus)
Seasoned developer (5+ years) within a modern business intelligence tool (Looker preferred)
Experience applying software engineering principles (testing, version control, code reviews, CI/CD, etc.) in your working process, to scale analytical development
Proven track record of working with data teams in a fast-paced, startup environment
Proven track record of successfully working cross-functionally with both technical and non-technical stakeholders
Solutions-driven and thrives when given ambiguous, open-ended problems
Proven success driving projects through implementation to completion, including stakeholder education and onboarding
Obsessed with data protections and privacy (i.e. PII, GDPR, FedRAMP, FIPS, etc.)

Nice To Haves

pipeline development a plus

Responsibilities

Architect, build and maintain efficient and reliable data products (dbt models, pipelines, Looker explores, visualizations, etc.) that enable data driven decision making.
Build and maintain ELT pipelines from source systems to data warehouses.
Influence strategy and drive execution of our analytical roadmap delivering self-service business intelligence at scale.
Provide leadership and mentorship to other engineers fostering collaboration, knowledge sharing and a high bar for quality.
Improve end users trust and confidence by driving best practices with regards to data discoverability, observability, lineage and stewardship.
Contribute towards a robust data security strategy protecting sensitive information and content.
Solve critical business problems through the application of programming, optimization, automation, and analytical skills.