Analytics Engineer

About the role

The Analytics Engineer designs, builds and owns Kisi's trusted, AI-ready data and insights layer. Rather than fielding individual data requests, the role makes sure that people and AI tools across the company get correct, authorized and context-aware answers on their own, and measures how accurate and impactful those answers are.

Responsibilities

1. Data activation in operational tools. Deliver relevant, trusted data into the tools each team already uses (e.g. enriching HubSpot with data from Stripe, Chargebee, the Kisi API and other sources) so teams can act without requesting reports.
2. Trusted insights layer. Build and maintain a documented, semantically modeled layer that humans and AI can query for accurate answers. Encode business context (such as the Stripe to Chargebee migration) and surface known data-quality limitations alongside results.
3. Consolidation of reporting assets. Migrate existing dashboards, reports and connected Sheets onto the insights layer to create a single source of truth.
4. Self-serve audiences and ad-hoc requests. Enable teams to build targeted audiences and one-off data pulls themselves, including through AI tools, and handle remaining business requests as they arise.
5. Source coverage. Integrate the business data sources needed for complete insights, such as Google Analytics, Facebook Ads and other marketing platforms.
6. Data governance and access control. Ensure every consumer, human or AI, can only access data they are authorized to see, with role-based access across the insights layer, dashboards and AI integrations.
7. Quality and impact measurement. Maintain an accuracy benchmark of known-correct figures and track adoption, time saved and decisions supported.
8. Success looks like: Team members get the insights they need, right when they need them, and trust them.

Qualifications

• Strong SQL and data modeling skills, including dimensional or semantic modeling in a cloud warehouse (BigQuery or similar)
• Experience building and maintaining data pipelines and integrations with SaaS tools such as HubSpot, Stripe and Chargebee
• Experience making data usable by AI tools and LLMs, including documentation, semantic layers and guardrails
• Working knowledge of data governance and role-based access control
• Ability to translate business questions into durable data products and to explain data limitations clearly to non-technical stakeholders