Data Scientist
Team: AdTech
Location: Canada, Remote, Shanghai
Commitment: Full time
Workplace Type: remote
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
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