Analytics Engineer

Chartmetric•New York, NY

2d•Remote

About The Position

● Lead the development of predictive analytics, anomaly detection systems, and data- driven insights for Chartmetric’s music intelligence platform, taking ownership of the entire data pipeline from schema design to delivery. ● Design automated data quality frameworks, scalable analytics solutions, and AI-powered insights to enhance artist performance tracking, audience engagement analysis, and market forecasting. ● Build real-time data monitoring systems to detect irregular streaming patterns, fraudulent activities, and unexpected shifts in music consumption. ● Optimize large-scale data pipelines, ensuring seamless integration of data from streaming platforms, social media, and audience engagement sources. ● Collaborate with product, engineering, and business teams to develop AI-driven search and analytics tools, making complex data easily accessible to industry professionals. ● Play a key role in data strategy and cross-functional collaboration, transforming raw data into actionable intelligence for artist managers, labels, and digital marketers. ● Specialize in advanced user data analytics and segmentation, developing sophisticated behavioral clustering models and customer journey analytics frameworks. ● Build and implement churn prevention analytics to drive subscription retention. ● Design and implement complex ETL pipelines specifically for user data integration across multiple platforms, creating a unified user data lake architecture that centralizes consumer information. ● Create dynamic segmentation models that automatically adapt to changing user behaviors and implement real-time cohort analysis frameworks to track segment evolution over time. ● Build cross-platform attribution models to measure marketing effectiveness across user segments, develop custom data visualization dashboards for user segment analysis, and create automated reporting systems to track segment performance metrics. ● Build ETL pipelines in Python to extract and transform data from streaming platforms (Spotify, Apple Music, YouTube) for artist performance analysis. ● Manage centralized data warehousing in Snowflake and AWS Redshift while utilizing PostgreSQL/BigQuery for relational data and MongoDB/Elastic Search for flexible user/music analytics. ● Implement high-performance analytics with Clickhouse for massive datasets and Kafka/Kinesis for real-time streaming data processing. ● Orchestrate data workflows with Airflow and developing predictive models using Scikit- learn, TensorFlow, and Snowpark to identify breakout tracks/artists. ● Create visualization solutions through Tableau, Looker, and Hex for executive dashboards and collaborative data exploration. ● Apply advanced statistical methods and machine learning algorithms to build ranking system for artists, creators and tracks to help prioritize the stats updating in Chartmetric. ● Use Hex to create collaborative Python/SQL workbooks to serve data needs and reduce insight time for faster decisions. ● Leverage the use of LLM APIs to create meaningful workflows using agentic RAGs for building LLM based applications.

Requirements

A Master’s degree in Analytics, Data Science or closely related field with 3 years of experience as an Analytics Engineer or Data Analyst position, which includes minimum of 3 years of experience with Python, Snowflake, PostgreSQL, Clickhouse, AWS, Airflow, Tableau, Looker, Hex, and LLM.
Telecommuting is permitted within the New York Metropolitan area.

Responsibilities

Lead the development of predictive analytics, anomaly detection systems, and data-driven insights for Chartmetric’s music intelligence platform, taking ownership of the entire data pipeline from schema design to delivery.
Design automated data quality frameworks, scalable analytics solutions, and AI-powered insights to enhance artist performance tracking, audience engagement analysis, and market forecasting.
Build real-time data monitoring systems to detect irregular streaming patterns, fraudulent activities, and unexpected shifts in music consumption.
Optimize large-scale data pipelines, ensuring seamless integration of data from streaming platforms, social media, and audience engagement sources.
Collaborate with product, engineering, and business teams to develop AI-driven search and analytics tools, making complex data easily accessible to industry professionals.
Play a key role in data strategy and cross-functional collaboration, transforming raw data into actionable intelligence for artist managers, labels, and digital marketers.
Specialize in advanced user data analytics and segmentation, developing sophisticated behavioral clustering models and customer journey analytics frameworks.
Build and implement churn prevention analytics to drive subscription retention.
Design and implement complex ETL pipelines specifically for user data integration across multiple platforms, creating a unified user data lake architecture that centralizes consumer information.
Create dynamic segmentation models that automatically adapt to changing user behaviors and implement real-time cohort analysis frameworks to track segment evolution over time.
Build cross-platform attribution models to measure marketing effectiveness across user segments, develop custom data visualization dashboards for user segment analysis, and create automated reporting systems to track segment performance metrics.
Build ETL pipelines in Python to extract and transform data from streaming platforms (Spotify, Apple Music, YouTube) for artist performance analysis.
Manage centralized data warehousing in Snowflake and AWS Redshift while utilizing PostgreSQL/BigQuery for relational data and MongoDB/Elastic Search for flexible user/music analytics.
Implement high-performance analytics with Clickhouse for massive datasets and Kafka/Kinesis for real-time streaming data processing.
Orchestrate data workflows with Airflow and developing predictive models using Scikit-learn, TensorFlow, and Snowpark to identify breakout tracks/artists.
Create visualization solutions through Tableau, Looker, and Hex for executive dashboards and collaborative data exploration.
Apply advanced statistical methods and machine learning algorithms to build ranking system for artists, creators and tracks to help prioritize the stats updating in Chartmetric.
Use Hex to create collaborative Python/SQL workbooks to serve data needs and reduce insight time for faster decisions.
Leverage the use of LLM APIs to create meaningful workflows using agentic RAGs for building LLM based applications.