Sr. Data Analytics Engineer

go Happy is hiring a Data Analytics Engineer to help build the data foundation that powers trustworthy reporting, self-serve insights, and decision-making across the company. This is a unique opportunity to join at the ground floor and own the core analytics engineering layer end-to-end, from defining foundational metrics and data models to implementing best practices that scale.

In this role, you’ll be both a hands-on builder and a technical partner to the business, designing data architecture, developing foundational models in dbt, and partnering with Product, Engineering, and GTM leaders to turn ambiguous questions into reliable, reusable data products.

This is a senior individual contributor role. You’ll lead through technical ownership, influence, and execution, while helping set standards and patterns that future hires will build on.

• Execute the analytics engineering roadmap by identifying the highest-leverage data opportunities and delivering the models and datasets needed to support them
• Design and build a single source of truth by developing foundational data models (dimensional modeling, star schemas, modular marts) that standardize business definitions and metrics
• Manage a modern analytics infrastructure by implementing and using tools like dbt, Snowflake, and Git, including CI/CD patterns
• Implement scalable standards for modeling, naming, testing, documentation, and dataset ownership to ensure maintainability as the organization grows
• Improve trust in data by implementing data quality checks, monitoring and observability practices, and clear guidance on what is trusted
• Partner cross-functionally with engineering, product, finance, and other business leaders to understand data needs and deliver solutions that drive decisions and outcomes

• Build curated datasets and semantic-friendly layers that keep BI and reporting consistent and reliable
• Create dashboards and analytics tools that empower stakeholders to self-serve confidently, with clear definitions and documentation
• Translate ambiguous business questions into structured, scalable data solutions, and not one-off queries
• Translate complex analytical findings into clear, actionable recommendations that influence product strategy

• Act as the analytics engineering voice in technical decisions, ensuring solutions are practical, robust, secure, and useful
• Drive cross-functional projects from discovery through delivery, influencing outcomes without formal authority
• Contribute to data literacy and adoption through metrics definitions, training, and strong documentation
• Collaborate with product managers during discovery to identify the right problems to solve using data

• 5 to 8+ years in analytics engineering, data engineering, BI, or closely related roles, with senior-level scope
• Advanced proficiency in SQL and cloud data warehouses (Snowflake preferred)
• Strong hands-on experience with dbt (or similar transformation frameworks), including modular project organization, plus testing and documentation practices
• Proven ability to design scalable, reusable data models (dimensional modeling, star schemas, canonical metrics, shared dimensions)
• Experience with BI tools (Looker, Tableau, Mode, or similar) and building datasets that support reliable reporting
• Working knowledge of Python for scripting, automation, and data transformation

• Comfort owning pipelines and data flows end-to-end (sources to warehouse to transformations to BI and downstream consumption)
• Familiarity with Git and Git Hub, code review workflows, and CI/CD practices for analytics code
• Experience implementing data quality and reliability patterns (dbt tests, Great Expectations, Monte Carlo, alerting, SLAs, or similar)
• Strong understanding of modern data architecture patterns and tradeoffs, with the ability to build what’s needed now while planning for scale

• Strong track record partnering with non-technical stakeholders, turning vague questions…