• 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.

• Please insert job responsibilities here

• Lead, mentor, and grow a team of data and analytics engineers • Own the curation, transformation, and delivery of analytics-ready data models • Build and maintain trusted, governed data assets • Oversee the development and delivery of Tableau and Sigma dashboards • Communicate technical roadmaps and delivery timelines to stakeholders

• Build and maintain high-quality data models that support reporting and analytics across the business • Transform and model new data sources using dbt, aligning with our data strategy and modelling standards • Partner with teams across the company to understand their data needs and enable data-driven decision making • Write clean, scalable SQL and contribute to sustainable, well-structured analytics code • Contribute to the continuous improvement of analytics engineering standards, documentation, and development workflows