Data Scienist

Responsibilities

• 1. Architecture & Development
• Design, build, and optimize data pipelines and ETL workflows in Snowflake using Snowpark, Streams/Tasks, and Snowpipe.
• Develop scalable data models, Algorithm 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.
• 2. Leadership & Collaboration
• 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 (PII).
• Collaborate with the Director of Data Platforms and product stakeholders to translate business requirements into technical specifications.
• 3. Performance & Scalability
• 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 (e.g., via Looker, Tableau, or Snowsight).
• 4. Governance & Best Practices
• 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.

Qualification & Skills

• 5+ years of experience in Data Scientist, 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.
• Preferred
• Experience lead machine learning teams or/and 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
• Strong ownership and ability to work autonomously in a fast-paced environment.
• Excellent cross-functional communication — can translate between engineering and business.
• Hands-on problem solver who balances velocity with reliability.
• Collaborative mentor who raises the bar for team quality and discipline