Senior Data Scientist: Scalable ML Pipelines & Data Eng

Lever, Inc.; Los Angeles (CA); On-site; USD 150,000 - 220,000; Full time

Benefits offered by this job

Medical, dental, vision

PTO

Career roadmap / development

Job summary

Xsolla, a global game commerce company based in Los Angeles, seeks a data-driven Data Scientist to build scalable data pipelines and ML feature inputs. You will lead cross-functional efforts with Data Science, ML, and Backend teams while ensuring data quality, governance, and privacy.

Strong ownership and autonomous work style are essential in a fast-paced environment. The role focuses on Snowflake-based architectures, real-time and batch ingestion, and collaboration with Legal, Security, and

Qualifications

• 5+ years of experience as a Data Scientist with 3+ years in Spark.
• Strong SQL and Python skills with experience building ETL/ELT at scale.
• Deep understanding of algorithm performance tuning and warehouse orchestration.
• Experience with data pipeline orchestration (Airflow, Prefect, dbt, or similar).

Responsibilities

• Architecture & Development: Design, build, and optimize data pipelines and ETL workflows in Snowflake using Snowpark, Streams/Tasks, and Snowpipe.
• Lead integration across data sources (MySQL, BigQuery, Redis, Kafka, GCP Storage, API Gateway).
• Mentor junior data engineers and collaborate with Data Science, ML, and Backend teams.
• Build dashboards and monitors for pipeline health and data latency metrics.

Skills

Data Scientist

Spark

SQL

Python

ETL/ELT

Data Modeling

Airflow

Big Data

Tools

Snowflake

Kafka

GCP

AWS

dbt

Looker

Tableau

Snowsight

Job description

Xsolla, a global game commerce company based in Los Angeles, seeks a data-driven Data Scientist to build scalable data pipelines and ML feature inputs. You will lead cross-functional efforts with Data Science, ML, and Backend teams while ensuring data quality, governance, and privacy.

Strong ownership and autonomous work style are essential in a fast-paced environment. The role focuses on Snowflake-based architectures, real-time and batch ingestion, and collaboration with Legal, Security, and