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

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.