Responsibilities

• Compose, build, and evolve analytics-ready marketing data models, dimensional schemas, and curated datasets.
• Develop scalable transformation frameworks using dbt, Databricks, and cloud-native technologies.
• Integrate and normalize data from advertising and social platforms, including Google, YouTube, Meta/Facebook, Instagram, and TikTok, accounting for differences in platform-specific metrics and definitions.
• Translate marketing and business requirements into scalable, reusable data models and semantic structures that support consistent measurement and reporting.
• Support the creation and upkeep of shared marketing taxonomies, metric frameworks, and naming conventions across data sources.
• Establish and apply standards for data quality, testing, documentation, lineage, and transformation-level controls.
• Partner to align upstream ingestion, including API-based data sources, with downstream modeling and consumption needs.
• Lead marketing analytics engineering workstreams from technical development through deployment and optimization. Serve as the analytics engineering contact for Marketing and influence decisions across teams without direct authority.
• Find opportunities to simplify the marketing data landscape, improve performance, and increase consistency and usability across marketing analytics products.

Requirements

• Significant experience developing and delivering marketing, media, or digital advertising data products in modern analytics environments.
• Strong hands-on expertise with SQL, dbt, and Databricks, as well as cloud data platforms such as Azure and/or AWS.
• Experience working with APIs from major advertising platforms, including the data ingestion, normalization, and quality challenges they introduce.
• Strong understanding of platform-specific marketing data and definitions and how they differ across ecosystems.
• Experience contributing to marketing taxonomies, metric frameworks, naming systems, or semantic standards across multiple data sources.
• Proficiency in key marketing measurement concepts: campaign and channel performance, attribution, audience segmentation, and CRM/first-party data.
• Deep understanding of dimensional modeling, semantic layer architecture, and data warehousing practices.
• Proficiency in Python or similar language for transformation, automation, validation, and API-related workflows.
• Experience implementing data quality frameworks, automated testing, validation processes, and documentation standards.
• Strong communication and collaboration skills, with the ability to translate complex marketing and data concepts into clear, business-aligned structures.

Preferred

• Experience supporting advanced analytics or AI-enabled marketing use cases is a plus.

Benefits

• Health & Wellness
• Time Off to Recharge
• Financial Well-being
• Life & Family Support
• Volunteer and Community Initiatives
• Learning & Development
• Exclusive Perks