Analytics Engineer

Movable Ink scales content personalization for marketers through data-activated content generation and AI decisioning. The world’s most innovative brands rely on Movable Ink to maximize revenue, simplify workflow and boost marketing agility. Headquartered in New York City with close to 600 employees, Movable Ink serves its global client base with operations throughout North America, Central America, Europe, Australia, and Japan.

Movable Ink scales content personalization for marketers through data-activated content generation and AI decisioning. The world’s most innovative brands rely on Movable Ink to maximize revenue, simplify workflow and boost marketing agility. Headquartered in New York City with close to 600 employees, Movable Ink serves its global client base with operations throughout North America, Central America, Europe, Australia, and Japan.

We're looking for a mid-level Analytics Engineer to sit within our Business Intelligence team and accelerate the speed at which we turn available data into insights. You'll serve as a key bridge between business stakeholders and our data models translating reporting needs into well-defined metrics and ensuring those definitions are reflected correctly across all systems.

On the modeling side, you'll work in dbt to build from our established silver layer to  consumption-ready gold layer in Redshift, operating within the standards and best practice. On the insights side, you'll build Tableau dashboards and reports, bring analytical rigor to conversations about what metrics matter and how they should be defined, and help stakeholders ask better questions of the data we already have.

This is not a full-stack data engineering role; you won't be ingesting new systems or building raw pipelines. You'll be a force multiplier on a rich, well-modeled data foundation, helping the team move faster and ensuring the right data gets in front of the right people. 

Responsibilities:

• Build and maintain dbt models from our business vault layer through to the data mart
• Add fields, refine calculations, and extend existing mart models in collaboration with the BI team and our Enterprise Data team
• Build net-new Kimball-style datasets from business vault sources when the data is available and no new ingestion is required
• Build and maintain AI-ready datasets designed for consumption by internal AI systems, ensuring data is structured, documented, and reliable enough to serve as a trusted source for machine learning and AI applications 
• Maintain and enrich our data dictionary in OpenMetadata, keeping definitions, lineage, and field-level documentation current as models evolve
• Partner with business stakeholders to define the right metrics for reporting; translating ambiguous business questions into clear, agreed-upon definitions before they become model changes
• Partner with BI Analysts to translate insight needs into modeling requirements, removing blockers so analysts can focus on analysis rather than data prep