Pega Data Analytics Engineer

Compass Pointe Consulting•Vienna, VA

About The Position

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. 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. 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.

Requirements

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

Nice To Haves

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

Responsibilities

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.