Freshpaint is a privacy-first data platform that helps healthcare organizations use modern marketing and analytics tools without exposing protected health information. The Senior Analytics Engineer will architect the foundational data systems to support the company's growth, focusing on designing a unified data model and enabling advanced analytics across teams.
Responsibilities:
- Architect the Unified Data Model
- Design and develop scalable, production-grade dbt models in Snowflake that serve as the single source of truth across the company
- Build analytics-ready datasets that eliminate fragmented metric definitions and align teams around consistent KPIs
- Establish modeling standards and conventions that scale as the company grows
- Optimize SQL and warehouse performance to balance reliability, scalability, and cost efficiency
- Expand and manage ingestion pipelines (Fivetran and others) to support new strategic data sources
- Enable Experimentation & Advanced Analytics
- Partner with Product and Engineering to ensure reliable event instrumentation and clean upstream data
- Build experimentation-ready datasets that support product iteration and growth analysis
- Develop datasets that power predictive insights, behavioral signals, anomaly detection, and intelligent recommendations
- Help translate analytical outputs into workflows and product experiences that drive measurable impact
- Build Data Products That Drive Decisions
- Define and evolve north star metrics and KPI frameworks aligned with company strategy
- Develop scalable Looker dashboards and curated data marts that enable self-serve analytics
- Translate complex datasets into clear, actionable narratives that influence roadmap prioritization and go-to-market strategy
- Reduce ad hoc requests by building durable, reusable analytics assets
- Establish Governance, Quality & Trust
- Implement governance frameworks, documentation standards, and metric definitions that reduce ambiguity
- Build automated freshness checks, anomaly detection systems, and testing frameworks within dbt
- Strengthen data reliability, discoverability, and cross-functional trust in analytics outputs
Requirements:
- 7+ years of experience in analytics engineering, data engineering, or business intelligence roles
- 3+ years of hands-on dbt experience building scalable, production-grade data models
- Deep SQL expertise and strong dimensional modeling fundamentals
- Experience working with modern data stacks (Snowflake, Fivetran, Looker)
- Experience supporting experimentation, predictive analytics, anomaly detection, or other advanced use cases
- Proven ability to implement governance practices and data quality controls
- Strong cross-functional collaboration skills; you translate technical complexity into business clarity
- Strong business acumen: you connect data architecture decisions directly to growth, product strategy, and revenue impact
- Comfort operating in ambiguity and proactively defining high-leverage initiatives
- Proficiency in Python for analytics, automation, or advanced analytical workflows
- Experience operationalizing ML outputs or advanced analytics into production workflows
- Familiarity with feature engineering or experimentation frameworks
- Experience with workflow orchestration tools (e.g., Airflow)
- Familiarity with GitHub and version control best practices
- Experience mentoring analytics engineers or fostering a data-driven culture