Data Warehouse Engineering Manager

Samsung•Los Angeles, CA

10d•$180,000 - $230,000

About The Position

The Samsung TV Plus group is seeking a Data Warehouse Engineer to support the build-out and scale of our Global Data Analytics & Insights (DAI) practice. This role is critical to ensuring that high-quality, trusted, and timely data flows from internal and third-party sources into our analytics ecosystem, enabling advanced analysis, ROI measurement, and asset-level performance reporting. You will partner closely with Analytics, Engineering, and Business stakeholders from Korea headquarters as well as regional offices to operationalize global data pipelines, ensure data accuracy and availability, and troubleshoot complex data quality issues. This role is foundational to enabling data-backed strategic decision-making and content investments, and the adoption of advanced AI-driven analytics capabilities.

Requirements

Bachelor’s degree in Data Science, Computer Science, Engineering, Statistics, or a related quantitative discipline.
8+ years of experience in data operations, analytics engineering, or a closely related analytics support role.
Strong proficiency in SQL and experience working with large, complex datasets.
Hands-on experience integrating internal and third-party data sources into cloud-based data platforms (AWS preferred).
Demonstrated ability to identify, troubleshoot, and resolve data quality and pipeline issues.
Strong analytical rigor, attention to detail, and comfort operating in a fast-paced, cross-functional environment.

Nice To Haves

Experience working with BI and visualization tools (e.g., Tableau, Power BI, Looker).
Familiarity with data orchestration and transformation tools (e.g., Airflow, dbt, or similar).
Exposure to ROI modeling, KPI frameworks, or asset-level performance analytics.
Strong written and verbal communication skills, with the ability to support executive-level analysis and storytelling.

Responsibilities

Own the ingestion and operationalization of internal and third-party data sources into centralized cloud storage (AWS S3) to enable enterprise analytics and insights.
Ensure the accuracy, timeliness, and reliability of data pipelines supporting ROI analysis, asset-level dashboards, KPI monitoring, and executive decision support.
Partner closely with the Engineering and Analytics teams across the globe to troubleshoot data quality issues and translate business requirements into scalable data solutions.
Enable high-stakes business insights and narrative-driven analyses by providing trusted, well-governed, and analysis-ready datasets.
Support the adoption of advanced analytics and AI capabilities by ensuring robust data foundations and operational excellence.