About the Role

Role details below.

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.
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.
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).
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.

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.
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.

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.

Medical, dental, and vision benefits.
PTO (Paid Time Off).
Personalized career roadmap for each employee.
Professional development through training and educational opportunities.
Support for employees and their families through a comprehensive Benefits Program.
Physical, mental, and emotional well-being support.

Structure: Collaborates with Data Science, ML, Backend, Legal, Security, and Infrastructure teams.

Background check will be conducted, including criminal history check, employment verification, and education verification.
Position involves accessing confidential company data.
Ensuring compliance with regulatory requirements is part of the role.
Applicants are encouraged to inquire about their rights under the Fair Chance Act.
Xsolla processes applicant data in accordance with its Privacy Notice for Job Applicants.
Artificial intelligence (AI) tools may be used to support parts of the hiring process, such as reviewing applications and analyzing resumes.
Final hiring decisions are made by humans, not AI tools.
Equal Employment Opportunity: Xsolla is an equal opportunity employer and does not discriminate based on race, color, religion, sex, national origin, age, disability, sexual orientation, gender identity, or any other characteristic protected by law.

Xsolla is hiring a Data Scientist