Leads the strategy and execution of a modern marketing data ecosystem, consolidating customer and campaign data into an Azure Databricks Lakehouse to power segmentation, personalization, and go-to-market execution. Owns data pipelines, CDP enablement, and lead management capabilities while modernizing legacy processes and ensuring high-quality, scalable, and reliable data for marketing activation. Partners cross-functionally to translate business needs into data engineering solutions that drive growth and operational efficiency. Responsibilities • Lead the consolidation of customer and marketing data into an Azure Databricks Lakehouse, ensuring scalability and accessibility • Design, build, and oversee data pipelines that integrate CRM, Martech, digital, and analytics data sources • Enable CDP capabilities including unified customer profiles, identity resolution, and audience segmentation • Support go-to-market execution by delivering reliable data for lead management, campaign activation, and reporting • Modernize legacy SQL-based list management processes into automated, governed data workflows • Establish and enforce data quality, validation, and testing standards, including ownership of UAT processes • Partner cross-functionally with Marketing, Analytics, Data Platform, and Martech teams to align data solutions with business needs • Translate marketing and business requirements into scalable data engineering solutions • Provide technical leadership while driving measurable business impact through improved data reliability and usability Qualifications To fulfill this role successfully, you must possess the following minimum qualifications and experience: • Strong experience with Azure Databricks and modern cloud data platforms, including building scalable Lakehouse architectures and data pipelines • Proficiency in SQL and PySpark, with the ability to work across both modern big data environments and legacy relational systems • Hands-on experience integrating CRM, Martech platforms, and digital data sources into unified data models • Deep understanding of marketing data domains, including lead management, customer lifecycle, segmentation, and campaign execution • Experience supporting and enabling Customer Data Platforms, identity resolution, and customer 360 initiatives • Proven ability to modernize legacy SQL-based workflows and manual list pulls into automated, governed pipelines • Familiarity with ETL/ELT tools and orchestration frameworks across both modern and traditional stacks • Experience establishing and maintaining data quality, validation, and testing processes across mixed technology environments • Knowledge of data governance, access controls, and compliance considerations spanning legacy systems and modern platforms • Ability to translate marketing and business requirements into scalable technical solutions across evolving data ecosystems • Strong collaboration and communication skills to work effectively with Marketing, Analytics, and Technology stakeholders It would be advantageous in this position for you to demonstrate the following capabilities and distinctions: • Advanced degree in a related field or relevant certifications in Azure, Databricks, or data engineering • Experience working in a hybrid data environment (Databricks alongside legacy data warehouses such as SQL Server) • Background in B2B marketing data ecosystems, including lead routing, scoring, and pipeline attribution • Prior experience supporting or implementing enterprise CDP platforms • Familiarity with MarTech stack integrations (Google/Adobe Analytics, etc.) • Experience with data orchestration and workflow tools (e.g., Azure Data Factory) in production environments • Knowledge of real-time or event-driven data processing for marketing use cases • Exposure to data privacy regulations and consent management (e.g., GDPR, CCPA) in marketing data workflows • Familiarity with Agile delivery models and leading data engineering teams in fast-paced environments • Experience influencing or driving data platform modernization initiatives at scale