• Design and implement a multi-tier SaaS reporting infrastructure to support diverse client levels and brand campaign requirements. • Lead the GA4 and Google Tag Manager strategy, overseeing implementation, QA, and the definition of event tracking standards for QR scans, lead captures, and promo engagement. • Architect and manage GA4 data exports into BigQuery, building robust models that join event data with account, venue, and subscription datasets. • Integrate data from diverse sources including Sanity CMS, Mailchimp, SMS platforms, and POS systems to create a unified view of the consumer journey. • Develop reusable reporting templates and automated dashboards for client recaps, monthly summaries, and real-time campaign performance. • Evaluate and deploy reporting automation tools such as Looker Studio Pro, AgencyAnalytics, or Whatagraph to enhance scalability. • Establish data governance and access control protocols to ensure secure, account-specific reporting views. • Partner with product and engineering teams to ensure tracking requirements are seamlessly integrated into the development lifecycle. • Translate complex analytics into actionable business insights for internal stakeholders, venues, and brand partners. • Maintain comprehensive documentation including tracking plans, data dictionaries, and architectural workflows.

• Deliver well-architected data solutions in partnership with data platform engineers, data governance, data science, and data analytics/visualization peers. • Partner with business teams to design data solutions with the ability to mentor early career data engineers. • Lead build-out and deployment of data pipelines. • Ensure reliability, scalability and governance of data pipelines.

• The Analytics Engineer sits at the heart of IEM's modern data stack, turning raw source data into the clean, well-modeled, business-ready datasets that power Tableau dashboards, executive decisions, and self-service analytics across Finance, Production, Supply Chain, and Engineering. • Working primarily in dbt and Snowflake, you own the transformation layer between ingestion and the BI surface: staging models, intermediate logic, dimensional models, tests, and documentation. • This is a hands-on individual contributor role with real ownership of production data models and a clear path into senior and principal analytics engineering as the team grows. • Partner with cross-functional stakeholders and the Business Intelligence team (Finance, Production, Supply Chain, Engineering) to translate operational needs into scalable data models and reliable metrics. • Author and maintain dbt tests, monitor freshness, investigate data quality issues end-to-end, and own resolution through to root cause. • Design, build, test, and document dbt models that turn raw Snowflake data into clean, reliable, analytics-ready datasets across Finance, Production, Supply Chain, and Engineering. • Build conformed dimensions, fact tables, and reporting models that balance performance, maintainability, and business user accessibility for Tableau dashboards and ad-hoc analysis.

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