Data Engineering Technical Lead, Samsung TV Plus

Samsung Electronics•Mountain View, CA

1d•$180,000 - $240,000

About The Position

Position Summary TV Plus Data Engineering Technical Lead DESCRIPTION: The Samsung global services team is responsible for driving Smart TV and mobile services businesses by building intelligent, integrated, cross-platform services that delight and entertain users. Samsung TV Plus is Samsung’s direct-to-consumer OTT video service featuring ad-supported linear channels and ad-supported VOD. In the US, TV Plus launched in April 2018 and is currently available on 2016-25 model Samsung Smart TVs and recent Galaxy mobile devices. As a data engineer, you will design, build, and maintain the infrastructure and systems that collect, process, and store large-scale data related to content, users, and advertising. The role is crucial for enabling data-driven decisions that shape product development, content strategy, and user experience.

Requirements

Bachelor's Degree required. Degree in Engineering or Computer Science preferred
10+ years of experience in roles like data engineering, data architecture which support a large-scale data and services
Expertise in major cloud platforms such as AWS, Azure, and Google Cloud, along with experience in large-scale data solutions
Deep understanding of data warehousing, ETL/ELT processes, SQL, and PySpark, Python, AWS services like Glue, Athena, Lambda, S3
Strong understanding and practical experience with data modeling, creating, and managing comprehensive data infrastructure.
Experience establishing and enforcing frameworks for data governance, compliance, security, and quality standards across enterprise systems.
Experience in writing, maintaining, and troubleshooting Terraform configurations and modules for cloud platforms
Experience collaborating with a globally distributed team

Nice To Haves

Optional qualifications include experience with building data pipelines for ML models, familiarity with ML frameworks like TensorFlow or PyTorch, and knowledge of MLOps practices.

Responsibilities

Create blueprints for data systems, including data models, schemas, and workflows, that serve as a roadmap for data engineers
Define and enforce data standards, policies, and best practices for data accuracy, integrity, and security across the organization.
Oversee the architecture, security, governance, and optimization of the data lake, ensuring data quality, accessibility, and compliance.
Develop and manage data pipelines that leverage APIs to extract, transform, and load data from internal and external sources into the data lake.
Continually evaluate effectiveness of existing workflows, systems, and integration points versus latest technologies
Bridge the gap between business stakeholders and data teams by translating complex business requirements into scalable technical data solutions.
Design and implement robust data integration and transformation processes to ensure seamless, governed large volumes of data flows across operational and analytical systems, ensuring data availability and reliability.
Evaluate and recommend appropriate technologies, platforms, and tools, including cloud services (mainly AWS), big data platforms (Hadoop, Spark), and databases.
Drive data quality initiatives by identifying systemic issues and implementing proactive solutions to improve data reliability and usability.
Automate the deployment and management of data pipelines and related resources using Terraform, ensuring efficient and consistent deployments across environments