Data Scientist

Skills

About the Role

Design, build, and optimize data pipelines and ETL workflows in Snowflake using Snowpark, Streams/Tasks, and Snowpipe. Develop scalable data models for user 360 views, churn prediction, and recommendation engine inputs; integrate diverse data sources; implement CI/CD and data quality checks; mentor junior engineers; support machine learning feature productionization; and establish governance, lineage, metadata standards, and streaming architecture practices.

Requirements

• 5+ years of experience in a Data Scientist role, including 3+ years with Spark
• Strong SQL and Python skills with ETL/ELT experience at scale
• Deep understanding of algorithm performance tuning, query optimization, and warehouse orchestration
• Experience with Airflow, Prefect, dbt, or similar orchestration tools
• Understanding of Kimball, Data Vault, or hybrid data modeling
• Proficiency with 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 such as Feast or Tecton
• Experience managing small data engineering teams and setting technical direction
• Strong ownership, autonomy, cross-functional communication, problem-solving, and mentoring skills

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 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
• Ensure compliance, privacy, and governance of user data with Legal, Security, and Infrastructure teams
• Translate business requirements into technical specifications with stakeholders
• Tune algorithm performance and establish partitioning, clustering, and materialized views
• Build dashboards and monitors for pipeline health, job success, and data latency
• Establish naming conventions, data lineage, and metadata standards
• Lead code reviews, enforce documentation standards, and manage schema versioning
• Contribute to the company’s data mesh and streaming architecture vision

Benefits

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

Hiring Process

Background check including criminal history, employment verification, and education verification.