Data Analytics Junior designing, developing and testing data processing solutions in a hybrid work environment. Handling data pipelines and collaborating with business teams.
About the role
- Senior Product Data Analyst defining metrics and insights for real estate products at Side. Collaborating with cross-functional teams to enhance product performance and data governance.
Responsibilities
- 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.
Requirements
- 4+ years in Product Analytics / Data Analytics for B2B SaaS (or equivalent impact), with a track record of driving measurable product and business outcomes.
- 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.
- Experience shaping warehouse schemas and data governance; collaborating with Data Engineering on ETL and reverse‑ETL; comfort reading/writing production‑grade SQL.
- 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.
- 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.
- Clear, concise storyteller who can translate complex analyses into simple, actionable decisions for executives and cross‑functional partners.
- Bias to action; comfortable operating as the sole Product analytics owner while partnering closely with Product, Design, Engineering, Data, and Finance.
- Experience in real estate, mortgage, title/escrow, or fintech is a plus.
- 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.
Benefits
- 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)
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