Analytics Engineer (first data hire)

Cofertility is a Series A company focused on the fast-growing field of fertility and family planning. We are a human-first fertility ecosystem rewriting the egg freezing and egg donation experience. Through Cofertility Freeze, women can freeze their eggs for free when they donate half of the retrieved eggs to a family who can’t otherwise conceive. Cofertility Family serves as a more transparent, ethical egg donor matching platform. We are passionate about improving the family-building journey — today or in the future — and are in an endless pursuit to make these experiences more positive. What We're Looking For We’re a mission-driven company powered by individuals who are fired up about making egg freezing and third party reproduction more accessible. Data sits at the center of how we build, grow, and personalize for our members - and we’re proud to work on something that genuinely matters. We’re hiring our first dedicated data hire to bring ownership, rigor, trust, and access to the data our team depends on every day. If you’re energized by building things from scratch and want your work to directly shape how a mission-driven company makes decisions, this one’s for you. You will own AI-assisted data analytics to democratize access to insights across the entire team. Responsibilities Own our data pipeline and platform, enabling data analytics across the organization Build and maintain an AI-ready semantic layer on top of dbt, so non-technical teams can query company data via natural language tools Own canonical metric definitions for all key business metrics, tested and documented in dbt Reconcile metrics across sources to establish a single source of truth Author and maintain dbt models across staging, intermediate, and mart layers using software engineering best practices Govern Amplitude instrumentation: define our event taxonomy, QA, and maintain our instrumentation spec Support experiment readouts with a consistent analysis framework and a clear point of view for A/B tests Use AI tools as a multiplier across analytics workflows (e.g., SQL authoring, dbt refactoring, to testing to root cause analysis.) You will have the latitude and expectation to experiment Modernize our analytics pipeline: deprecate legacy dependencies and build alerting and monitoring infrastructure Build and maintain channel attribution mapping - UTM governance, cross-channel QA, and attribution consistency Professional Characteristics We are hiring for the skills, the mindset, and the potential to grow into this role and beyond it. 3 - 6 years in analytics, data, or analytics engineering; startup or high-growth experience preferred A structured thinker who traces problems upstream and builds systems rather than point fixes. You can take an ambiguous stakeholder question and produce a definition, a model, and an insight. You do not wait to be told what to measure. Strong SQL: DRY, performant, readable; you write CTEs and window functions naturally and can explain why your query is structured the way it is Fluent in dbt: you understand model layer conventions and treat analytics code like software code Deep experience with Amplitude: You’ve governed instrumentation at scale, not just queried events Actively using AI tools in your analytics workflow (Cursor, Claude, dbt Copilot, or similar) Familiarity with Prefect, Metabase, and BigQuery a plus Comfortable reading Go (or similar backend) code for the purpose of scoping data tables, understanding event structures, and proposing instrumentation - you don't need to write it, but you can navigate it Personal Characteristics Ownership-oriented - you define the standard, you don’t wait to be handed one Intellectually curious and always learning A clear communicator who can make data complexity accessible to non-technical partners Comfortable with ambiguity and energized by building from scratch Collaborative and kind - you take the work seriously without taking yourself too serio ... (truncated, view full listing at source)