Marketing Data Analytics Engineer building Salesforce, Marketo, and warehouse pipelines for PointClickCare’s healthcare analytics platform. Automating campaign reporting and supporting marketing - sourced pipeline insights.
About the role
- Analytics Engineer for Narvar focused on building data infrastructure and enabling internal analytics. Collaborating with stakeholders to deliver metrics, models, and self-serve analytics solutions.
Responsibilities
- Partner with product, go-to-market, and executive stakeholders — running discovery on ambiguous questions and scoping the metrics and data they actually need
- Raise data trust — adding the validation, definitions, and documentation that let users rely on the numbers and our tooling
- Expand and own our semantic / metrics layer — defining and maintaining metric definitions and models so analytics are consistent, trustworthy and reusable across the company
- Deliver self-serve and AI-accessible analytics — curated datasets, metrics, and reporting that internal partners and our agentic / LLM querying surface can answer on their own
- Ingest net new data — designing and building pipelines to bring in new sources such as GTM and product-usage data and modeling them for analytics
Requirements
- 3+ years in analytics engineering, data, or a closely related role, including ownership of metrics or data models that other teams rely on
- Deep SQL and hands-on data modeling — dimensional modeling, incremental transformations, and a feel for clean, maintainable models
- Proven experience building and expanding a semantic / metrics layer — its models, definitions, and context — that other teams adopt; you’ve owned what others depend on rather than consumed it
- Extensive hands-on experience using Claude/Codex for analytics — you’ve done substantive analytical work with it and know how to structure data, metrics, and metadata so it answers reliably
- The ability to stand up a new data source end to end — comfort with orchestration, APIs, and batch ETL, not just querying what already exists
- Excellent stakeholder communication — you can lead a conversation with a non-technical partner, walk away with a data spec, and explain a metric so people trust it
- A builder’s mindset — you’re motivated by creating durable, reusable metrics and self-serve infrastructure that scales beyond any single request
- Working knowledge of a cloud data warehouse (GCP / BigQuery preferred), a BI tool such as Looker, and Python for pipeline and tooling work.
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