BASIC QUALIFICATIONS:

• 2+ years of experience in Analytics Engineering or Decision Science in a high-scale environment, along with expert-level SQL skills, experience in BigQuery or other cloud-based data warehouses, and proficiency in Python/R or other scripting languages for data manipulation and statistical modeling.
• Strong experience optimizing large-scale data queries and pipelines for performance and efficiency, along with experience building Decision Science testing or statistical modeling for A/B testing.
• Strong analytical mindset with experience in A/B testing frameworks and experimentation methodologies, along with the ability to optimize large-scale data queries and pipelines.

ADDITIONAL QUALIFICATIONS:

• Strong communication skills to collaborate with ML engineers, data scientists, and product teams.
• Hands-on experience with dbt (Data Build Tool) for transformation workflows.
• Knowledge of data governance and quality best practices.
• Familiarity with Google Cloud Platform (GCP), BigQuery, and Looker.
• Experience working with personalization and recommendation systems.
• Strong problem-solving skills and a passion for Decision Science methodologies.
• Interest in open-source contributions and industry standard processes for analytics engineering.

OVERVIEW AND RESPONSIBILITIES:

The Applied Intelligence Personalization Team at Paramount is looking for a Data Engineer - Engagement & Experimentation to join our team. This role will focus on analyzing engagement metrics, evaluating experiment results, and optimizing our recommendation and personalization strategies. The ideal candidate will be an expert in SQL, Python/R, streaming data processing, and visualization tools like Apache Superset, ensuring that insights drive impactful decision-making.

RESPONSIBILITIES INCLUDE:

• Design and maintain data pipelines and analytics frameworks to track engagement metrics and A/B test results.
• Assist in data validation, feature engineering, and exploratory data analysis.
• Query, clean and analyze large datasets with an emphasis on statistical analysis.
• Develop and optimize Superset dashboards and reports for experimentation insights and KPI tracking.
• Communicate results and recommendations to technical and non-technical stakeholders.
• Work with large-scale structured and semi-structured datasets, designing appropriate decision science tests for analytics and reporting.
• Monitor and analyze A/B testing results to optimize personalization models and content recommendations.
• Implement standard processes for statistical testing, quality, and documentation.

Key Projects:

• Develop real-time engagement tracking and experimentation analysis pipelines.
• Improve A/B testing frameworks for personalized user experiences.
• Optimize streaming data workflows for user behavior analysis.
• Enhance Python scripting to support data-driven decision-making.