Data Engineer Jobs

About The Position

Requirements

• Mid to senior-level experience who can work independently, make architectural decisions, and build data systems from the ground up.
• Strong data engineering fundamentals.
• Experienced with SQL, Python, and building data pipelines.
• Understand data modeling, ETL/ELT patterns, and how to design scalable data architectures.
• Worked with databases like PostgreSQL and have opinions on data infrastructure tools.
• Comfortable with Go.
• Can drive technical decisions about data infrastructure.
• Have evaluated and implemented data tools before—whether orchestration frameworks, warehouses, transformation tools, or BI platforms.
• Can assess our needs and recommend the right stack for an early-stage company.
• Comfortable working with diverse data sources.
• Will work with operational databases, event streams (Redis), document stores (Elasticsearch), API data, and LLM outputs.
• Can navigate different data formats and build reliable integrations.
• Comfortable working closely with product teams.
• Have worked embedded with product and engineering, participated in product discussions, and built data systems that directly power product features.
• Understand the difference between building for analysis versus building for production product use.
• Can build both analytical and operational data pipelines.
• Have built pipelines for analytics and business intelligence, but you've also built systems that feed data back into products in real-time or near-real-time.
• Understand the different requirements and tradeoffs.
• Some experience with AI/ML data.
• Have worked with LLM outputs, unstructured data, or analytics for AI-powered products.
• Understand the unique challenges of working with AI-generated content and can design appropriate data models.
• Work well independently in an early-stage environment.
• Comfortable with ambiguity and can prioritize work without extensive direction.
• Can balance building for immediate needs while creating foundations for future scale.

Nice To Haves

• Comfortable with Go. Our backend stack is Ruby and Go, with new development preferentially in Go. While we are happy to use Python as the primary language for data work, comfort with Go is a plus for building production data services and working closely with the backend team.

Responsibilities

• Build data pipelines for coaching and user analytics. You'll create pipelines that process coaching session data, user interactions, and AI-generated outputs. These pipelines will power everything from product analytics to coaching effectiveness metrics to business intelligence dashboards.
• Build data systems that power product features. You'll create data pipelines that feed back into the product—user progress tracking, coaching insights, customer dashboards, and more. You'll work with product and engineering to understand what data products need and build reliable systems to deliver it.
• Establish our data infrastructure and architecture. As our first data engineer, you'll make key decisions about our data stack—choosing and implementing tools for orchestration, transformation, warehousing, and analytics. You'll build foundations that can scale with the company.
• Process and analyze AI/LLM outputs. You'll work with data from our AI coaching systems, building pipelines to analyze LLM responses, track coaching quality, and generate insights about how users interact with AI coaches.
• Support business operations and analytics. You'll build data systems that track customer success metrics, billing data, user engagement, and operational KPIs. Your work will directly inform business decisions and product strategy.
• Create accessible analytics infrastructure. You'll build systems that make data available to non-technical stakeholders through dashboards, reports, and self-service analytics tools. You'll think about data quality, documentation, and usability.
• Work closely with product on instrumentation and data collection. You'll collaborate with product and engineering teams to instrument new features, define events and metrics, and ensure we're collecting the right data. You'll be involved in product discussions and understand feature requirements from a data perspective.

Benefits

• Competitive salary and equity
• Medical, dental, vision, and a 401k plan
• Unlimited vacation
• $500 home office stipend
• Access to your own Mento Coach