Data Analytics Engineer - Pega CDH / Databricks

We are seeking a highly analytical and technically skilled Senior Data Analytics Engineer to support the development, implementation, and monitoring of advanced Customer Decision Hub (CDH) modeling capabilities. This role will focus on enabling faster analysis, improving data accessibility, and standardizing analytical processes across enterprise marketing and analytics teams., This position will partner closely with analytics, modeling, marketing, and decisioning teams to create scalable analytical frameworks, reusable notebooks, and actionable monitoring solutions that improve customer engagement and model effectiveness., * Develop and maintain a library of reusable queries, scripts, and analytical assets to replicate CDH customer contextual objects within external analytical platforms such as Databricks and ASL.

• Standardize analytical processes and data retrieval methodologies for broader team usage and consistency.
• Build scalable data pipelines and analytical frameworks using Python/PySpark and SQL.
• Create reusable Databricks notebooks that enable self-service analytics across multiple business functions.
• Develop standardized analytical solutions for interaction-to-outcome attribution analysis, model-to-interaction mapping, predictor performance tracking, member profile mapping, distribution analysis, arbitration analysis, and channel engagement analysis.
• Support implementation and analysis of new model-related capabilities and features.
• Establish baseline KPIs and monitoring frameworks for new modeling initiatives.
• Design and support back-testing methodologies for model enhancements and propensity threshold analysis.
• Monitor model maturity, performance trends, and operational effectiveness.
• Develop near real-time monitoring approaches to identify low propensity scores, ineffective actions, and engagement gaps.
• Improve visibility into model health and Next Best Interaction (NBI) program effectiveness.
• Design analytical frameworks for eligible audience monitoring and treatment analysis.
• Correlate interactions with demographic and behavioral data for deeper customer insights.

The ideal candidate will have strong experience working within Databricks environments using Python/PySpark and SQL, along with expertise in large-scale data analysis, model performance monitoring, and customer interaction analytics. Experience with Pega Customer Decision Hub (Pega CDH) is strongly preferred., * Bachelor’s degree in Computer Science, Data Analytics, Information Systems, Mathematics, Statistics, or related field (or equivalent experience)

• Strong hands-on experience with Databricks environments
• Advanced proficiency in Python, PySpark, and SQL
• Experience building reusable analytical frameworks and notebooks
• Experience performing large-scale data analysis and data modeling
• Strong understanding of model performance monitoring and KPI development
• Ability to translate business questions into scalable analytical solutions

Preferred Qualifications

• Experience with Pega Customer Decision Hub (Pega CDH)
• Experience supporting marketing analytics, customer engagement, or decisioning platforms
• Familiarity with propensity models, arbitration logic, and customer interaction analytics
• Experience with monitoring frameworks and real-time analytical alerting
• Understanding of customer journey analytics and Next Best Action/Interaction programs
• Experience working in enterprise analytics or customer intelligence environments

Key Skills

Databricks, Python, PySpark, SQL, Data Engineering, Data Analytics, Model Monitoring, KPI Development, Customer Analytics, Predictive Modeling, Marketing Analytics, Notebook Development, Data Standardization, Decisioning Analytics, Stakeholder Collaboration

Success Metrics

• Creation of reusable and scalable analytical assets for enterprise use
• Reduction in time required to conduct CDH and model analysis
• Improved visibility into model health and customer engagement effectiveness
• Increased consistency and repeatability of analytical processes
• Enhanced ability for teams to perform self-service analytics and monitoring