We are looking for a Staff Analytics Engineer to lead the modelling and semantic foundation of our Customer Data Platform. This role sits at the intersection of product data, analytics engineering, and data platform architecture. You will define how product events become structured behavioural datasets that power analytics, product insights, machine learning, and in-app reporting. You will partner closely with product, engineering, marketing, data science, and platform teams to ensure that behavioural data is reliable, well-modelled, and consistently defined across the company.
Responsibilities
- Define and govern the product event taxonomy across services and applications.
- Partner with engineering teams to establish clear instrumentation contracts and naming standards.
- Own the modelling patterns that translate event collection pipelines into durable warehouse datasets.
- Ensure event data is reliable, deduplicated, and usable for analytics and modelling.
- Transform raw events into reusable behavioural datasets such as sessions, feature usage, funnels, retention cohorts, and customer journeys.
- Design models that enable product teams to analyse feature adoption, engagement, and lifecycle behaviour.
- Maintain modelling patterns that support both exploratory analysis and production use cases.
- Define and maintain canonical entities such as Agency, Location, Contact, Conversation, Campaign, Spend, Usage, and Outcomes.
- Establish durable fact and dimension models that connect behavioural events to business entities.
- Ensure relationships between entities remain consistent and scalable across teams and product surfaces.
- Build warehouse models that power product analytics platforms.
- Ensure metrics in analytics tools and warehouse metrics resolve to the same definitions.
- Provide standardised datasets for funnels, cohorts, retention analysis, and product experimentation
- Build behavioural and feature-ready datasets used by data science for lifecycle modelling, experimentation, and prediction.
- Ensure datasets are stable, versioned, and reproducible for downstream ML workflows.
- Establish modelling patterns, dbt conventions, macros, and documentation standards used across analytics engineering.
- Design tenant-safe models that support multitenant workloads and high-concurrency analytics.
- Partner with platform teams to ensure models are performant for both internal analytics and in-app experiences.
- Define tests, freshness expectations, and invariants for behavioural datasets.
- Implement automated validation for event completeness and schema consistency.
- Partner with platform and engineering teams to detect and resolve issues before they impact analytics or customers.
- Establish reusable modelling patterns and best practices.
- Review work from analytics engineers and raise the bar for correctness, clarity, and maintainability.
- Help shape the longterm architecture of the behavioural data platform.
Requirements
- 9+ years in analytics engineering, data engineering, or data architecture.
- Deep expertise in SQL and dbt, including testing, documentation, and version-controlled workflows.
- Strong experience modelling event-based or product usage data at scale.
- Experience working with modern event collection systems and product analytics platforms.
- Proven ownership of canonical datasets or semantic layers used by multiple teams.
- Strong judgment around metric definitions, change management, and keeping data consistent across a growing platform.
Success In This Role Looks Like
- Product events across the platform follow a clear and consistent taxonomy.
- Event collection pipelines feeding the warehouse, and OLAP systems produce reliable, analysis-ready behavioural data.
- Product analytics tools, internal analytics, and customer-facing reporting all resolve to the same underlying definitions.
- Product teams can analyse usage, funnels, and retention without building custom analytics logic.
- Data science teams rely on stable behavioural datasets rather than raw event streams.
- Canonical customer and product models become the default foundation for analytics and product features across HighLevel.
This job was posted by CS Srinivas Rao from HighLevel.
