Senior Product Data Analyst

At Side, we believe everyone should own their path.

Side partners with top-producing real estate professionals to help them own and operate their own boutique real estate companies, without the legal, regulatory, or operational complexity of running a traditional brokerage. As a pioneer in the Brokerage-as-a-Service model, Side provides a proprietary platform that increases efficiency, strengthens client relationships, and enables entrepreneurs to focus on what they do best — selling real estate and growing their businesses.

Headquartered in San Francisco and backed by more than $300 million from top-tier venture capital firms, Side is recognized as one of the most innovative and fastest-growing companies in real estate. At Side, you’ll work alongside experienced industry leaders and world‑class engineers to help shape the future of real estate and empower exceptional professionals to thrive as business owners.

The Product team at Side drives product vision, strategic planning, and the design, rollout, and measurement of new products and features. We deliver products that delight customers and impact the business, and we measure that impact against well‑defined success outcomes. We build iteratively based on data‑directed insights and user feedback, and we collaborate deeply with internal partners and customers to learn quickly and execute with transparency.

This role sits in the Product org and partners closely with Product, Design, Engineering, and our Data team (you’ll attend key Data ceremonies) to ensure we instrument, govern, and activate our data to drive measurable outcomes.

We’re hiring a hands‑on Senior Product Data Analyst to be the Product org’s one‑stop shop for deep product analytics. You will define the metrics that matter, own event taxonomy and instrumentation across our products, and build the semantic layer and dashboards that power decision‑making. You will also shape our long‑term data governance and warehouse strategy in partnership with Data Engineering, and synthesize qualitative and quantitative insights for executives and product teams.

You will work in a complex and evolving problem space, which requires comfort with unknowns, flexibility with analysis modalities, and strong initiative, as well as the ability to manage priorities across multiple stakeholders.

Roughly 65% of your time will focus on product analytics (behavioral usage, funnels, feature performance, experimentation) and 35% on business analytics (growth, forecasting, portfolio/agent performance).

You’ll manage core analytics tools (Looker, Pendo, Big Query), coach colleagues on best practices, and act as the DRI for product analytics from instrumentation through insight.

• Define & Instrument Product Metrics:
  Partner with PMs, Designers, and Engineers to define KPIs, event schemas, and experiment designs, with a focus on instrumenting front‑end tracking for user behavior data; lead Pendo event/taxonomy implementation, QA, and ongoing governance; build and maintain Looker reports and dashboards for stakeholders in all orgs.
• Build the Analytics Layer:
  Design and maintain Looker Explores, Looks, and dashboards; manage LookML and semantic modeling; set up advanced LookML data structures/models; ensure consistent definitions across teams.
• Synthesize & Communicate Insights:
  Blend quantitative and qualitative inputs (e.g., Pendo and other usage data, feedback, research) into clear narratives and recommendations for leadership and product squads.
• Own Data Governance:
  Establish naming conventions, documentation, and data quality standards for product analytics (including UTM standards, user/agent segmentation, and guide/feature metadata).
• Coach & Uplevel:
  Run office hours, trainings, and reviews to improve analytics acumen across Product/Design/Engineering; collaborate with Finance Analytics where roadmaps intersect.
• Warehouse & Pipelines:
  Collaborate with Data Engineering to shape Big Query schemas and marts; contribute SQL for production pipelines; partner on ETL/reverse‑ETL workflows and SLAs; monitor with Data Dog and alert on data health.
• Experimentation:
  Define and…