Data Scientist

You will design, build, and optimize data pipelines and ETL workflows in Snowflake using Snowpark, Streams/Tasks, and Snowpipe. You will develop scalable data models supporting user 360 views, churn prediction, and recommendation engine inputs, and lead integration across data sources including MySQL, BigQuery, Redis, Kafka, GCP Storage, and API Gateway. You'll implement CI/CD for data pipelines using Git and dbt, and define data quality checks and auditing pipelines. You will mentor and guide junior data engineers, partner with Data Science, ML, and Backend teams to productionize machine learning features, and collaborate with Legal, Security, and Infrastructure teams on compliance and governance of user data. You will tune algorithm performance, establish data partitioning, clustering, and materialized views, and build dashboards to monitor pipeline health. You'll also establish naming conventions, data lineage, and metadata standards, lead code reviews, and contribute to the company's evolving data mesh and streaming architecture vision.

Responsibilities

• Design, build, and optimize data pipelines and ETL workflows in Snowflake using Snowpark, Streams/Tasks, and Snowpipe
• Develop scalable data models supporting user 360 views, churn prediction, and recommendation engine inputs
• Lead integration across data sources: MySQL, BigQuery, Redis, Kafka, GCP Storage, and API Gateway
• Implement CI/CD for data pipelines using Git, dbt, and automated testing
• Define data quality checks and auditing pipelines for ingestion and transformation layers
• Mentor and guide junior data engineers on data modeling, performance tuning, and Snowflake best practices
• Partner with Data Science, ML, and Backend teams to productionize machine learning features in Snowflake
• Work closely with Legal, Security, and Infrastructure teams to ensure compliance, privacy, and governance of user data
• Collaborate with the Director of Data Platforms and product stakeholders to translate business requirements into technical specifications
• Tune algorithm performance
• Establish data partitioning, clustering, and materialized views for fast query execution
• Build dashboards and monitors for pipeline health, job success, and data latency metrics
• Establish and enforce naming conventions, data lineage, and metadata standards across schemas
• Lead code reviews, enforce documentation standards, and manage schema versioning
• Contribute to the company's evolving data mesh and streaming architecture vision

Requirements

• 5+ years of experience in Data Scientist role, with 3+ years in Spark framework
• Strong SQL and Python skills, with proven experience building ETL/ELT at scale
• Deep understanding of algorithm performance tuning, query optimization, and warehouse orchestration
• Experience with data pipeline orchestration (Airflow, Prefect, dbt, or similar)
• Solid understanding of data modeling (Kimball, Data Vault, or hybrid)
• Proficiency in Kafka, GCP, or AWS for real-time or batch ingestion
• Familiarity with API-based data integration and microservice architectures
• Experience leading machine learning teams or deploying ML feature pipelines
• Background in ad-tech, gaming, or e-commerce recommendation systems
• Familiarity with data contracts and feature stores (Feast, Tecton, or custom-built)
• Experience managing small data engineering teams and setting technical direction

Benefits

• Medical, dental, and vision insurance
• PTO
• Personalized career roadmap
• Professional development through training and educational opportunities