Senior Product Data Analyst
Side
Product, IT, Data Science
San Francisco, CA, USA
Posted on Nov 18, 2025
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.
About the Team
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.
About the Role
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, BigQuery), coach colleagues on best practices, and act as the DRI for product analytics from instrumentation through insight.
What You’ll Be Doing:
- 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 BigQuery schemas and marts; contribute SQL for production pipelines; partner on ETL/reverse‑ETL workflows and SLAs; monitor with DataDog and alert on data health.
- Experimentation: Define and support A/B and multi‑variant tests (design, guardrails, power, analysis) and drive a culture of measurement and learning.
- Tool Stewardship: Administer Looker licensing and workspace hygiene, manage Pendo taxonomy and implementation, and coordinate with vendors; maintain access, permissions, and usage dashboards.
- Backlog & Roadmapping: Maintain a product analytics roadmap and intake process; prioritize the highest‑impact analytics work and instrument future features ahead of launch.
What Will Make You a Strong Fit:
- Experience: 4+ years in Product Analytics / Data Analytics for B2B SaaS (or equivalent impact), with a track record of driving measurable product and business outcomes.
- Technical Skills: Expert SQL (multiple variants, including BigQuery & Postgres); strong LookML/Looker modeling; hands‑on Pendo (or Heap, or equivalent) instrumentation and taxonomy governance; familiarity with DataDog for metrics/alerting.
- Deep experience managing and working with front-end tracking data, user behavior data, workflows, segmentation, and analysis.
- Experience working within datamart design and implementation for self-service enablement.
- Advanced analytics modeling (predictive, forecasting, churn, ML models) to apply in either a product or business context.
- Data Strategy: Experience shaping warehouse schemas and data governance; collaborating with Data Engineering on ETL and reverse‑ETL; comfort reading/writing production‑grade SQL.
- Product Depth: Strong desire to understand a product and users end-to-end, to drive context for data and for recommending how we can use data to make improvements.
- Experimentation & Methods: Practical understanding of A/B testing, metric guardrails, and statistical inference; ability to choose pragmatic methods for real‑world product questions; experience working within strong data constraints and limited data availability.
- Communication: Clear, concise storyteller who can translate complex analyses into simple, actionable decisions for executives and cross‑functional partners.
- Ownership & Collaboration: Bias to action; comfortable operating as the sole Product analytics owner while partnering closely with Product, Design, Engineering, Data, and Finance.
- Domain Bonus: Experience in real estate, mortgage, title/escrow, or fintech is a plus.
- Education: B.A./B.S. or equivalent practical experience in a quantitative field (e.g., Statistics, Economics, Computer Science, Engineering).
- Nice‑to‑Haves: Python or R; dbt; BigQueryML; Airflow/Orchestration; GCP experience beyond BigQuery; exposure to feature flag platforms and event streaming.
Tools You’ll Use:
- Looker, Pendo, BigQuery, DataDog (plus standard productivity tools). You’ll also collaborate on our ETL/reverse‑ETL stack and contribute SQL to production data models as needed.
Compensation/Perks:
- Competitive salary
- Stock options
- Best‑in‑class benefits, including healthcare coverage (medical, vision, dental)
- Flexible PTO
- Learning & Development credit
- Hybrid work: in‑office 2 days/week (SF office is pet‑friendly)
Side is dedicated to working with the highest skilled people from the most inclusive talent pool feasible. We maintain that diversity in all aspects leads to positive change, solutions and innovation for our customers and career fulfillment for our employees. All qualified individuals are encouraged to apply!
Side uses the E-Verify employment verification program.
Our stewardship of the data of many of our customers means that a background and DRE license check is required to join Side. We will, nonetheless, consider qualified applicants with arrest and conviction records in accord with applicable law, including the San Francisco Fair Chance Ordinance.
Side takes a market-based approach to pay, and pay may vary depending on your location. This range is not inclusive of our equity package. When determining a candidate’s compensation, we consider a number of factors including skillset, experience, job scope, and current market data.