Chartmetric

● 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.

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