Staff Engineer I, Data Engineering

Samsung Electronics•Irvine, CA

9d•Hybrid

About The Position

Position Summary Role and Responsibilities MULTIPLE POSITIONS AVAILABLE Company: Samsung Electronics America, Inc. Position Title: Staff Engineer I, Data Engineering Location: Irvine, CA Job ID: SAM9441217 Position Responsibilities: Design and develop scalable data stores and frameworks with sub-second query latency on highly multi-dimensional data. Provide engineering solutions to aggregate and automate large scale data flows from varying sources. Build real time streaming pipelines that deliver data with measurable quality under the SLA. Ability to effectively communicate ideas to peers and distributed teams. Deliver products with top notch quality in a fast-paced environment. Contribute towards building a system with a test-driven development and agile approach. Collaborate with other team members in breaking down tasks and implementation of the initiatives to release. Architect and implement robust data services that allow for efficient, scalable and reliable data collection, processing and transformation across multiple systems, ensuring the data is available for analytics and business applications. Develop and maintain highly efficient, fault tolerant ETL/ELT pipelines to handle large datasets, enabling seamless integration with downstream analytics, machine learning models, and reporting systems. Implement solutions for real-time data ingestion, streaming, and processing to deliver up-to-date business applications. Design, implement and manage distributed data storage solutions. Work closely with data governance teams to ensure that data services meet all compliance, privacy and security standards (eg. GDPR, CCPA). Implement policies and best practices for data management, lineage and quality. Continuously monitor and optimize data processing performance, including optimizing data storage, query performance and the scalability of data pipelines. Collaborate with product managers, business stakeholders and other engineering teams to understand data needs and provide high quality data services. Foster a culture of continuous learning and best practices. Create and maintain documentation for data services, pipelines and architectures. Establish and promote best practices for data engineering, automation and scalability across the team.

Requirements

Bachelor’s degree or foreign equivalent degree in Computer Science, Computer Engineering, Information Networking, Data Science or a related field and ten (10) years of progressive post-baccalaureate experience as a Staff Engineer I, Data Engineering or related occupation.
Must have ten (10) years of experience in the following skills: (1) experience with large scale distributed systems as pertains to data storage and computing; (2) experience with Amazon AWS technologies, including S3, EMR, and Redshift; (3) experience with databases, including Oracle, Postgres, Cassandra, and Vertica; (4) experience with data technologies, including Airflow, Spark, Python, SQL, Java, AWS, and Computer Science Fundamentals; (5) experience with data modeling, ETL development, and data warehousing, and Object Oriented Design and Development (6) experience in implementing, and maintaining CI/CD pipelines using various tools and technologies, including understanding the different stages of a pipeline.
Employer will accept a Master’s degree or foreign equivalent degree in Computer Science, Computer Engineering, Information Networking, Data Science or a related field and eight (8) years of experience as a Staff Engineer I, Data Engineering or related occupation as equivalent to the Bachelor’s degree and ten (10) years of experience.
If qualifying with a Master’s degree or foreign equivalent degree, must have eight (8) years of experience in the following skills: (1) experience with large scale distributed systems as pertains to data storage and computing; (2) experience with Amazon AWS technologies, including S3, EMR, and Redshift; (3) experience with databases, including Oracle, Postgres, Cassandra, and Vertica; (4) experience with data technologies, including Airflow, Spark, Python, SQL, Java, AWS, and Computer Science Fundamentals; (5) experience with data modeling, ETL development, and data warehousing, and Object Oriented Design and Development (6) experience in implementing, and maintaining CI/CD pipelines using various tools and technologies, including understanding the different stages of a pipeline.
Partial telecommuting permitted; employees will be required to report to office multiple days per week.

Responsibilities

Design and develop scalable data stores and frameworks with sub-second query latency on highly multi-dimensional data.
Provide engineering solutions to aggregate and automate large scale data flows from varying sources.
Build real time streaming pipelines that deliver data with measurable quality under the SLA.
Ability to effectively communicate ideas to peers and distributed teams.
Deliver products with top notch quality in a fast-paced environment.
Contribute towards building a system with a test-driven development and agile approach.
Collaborate with other team members in breaking down tasks and implementation of the initiatives to release.
Architect and implement robust data services that allow for efficient, scalable and reliable data collection, processing and transformation across multiple systems, ensuring the data is available for analytics and business applications.
Develop and maintain highly efficient, fault tolerant ETL/ELT pipelines to handle large datasets, enabling seamless integration with downstream analytics, machine learning models, and reporting systems.
Implement solutions for real-time data ingestion, streaming, and processing to deliver up-to-date business applications.
Design, implement and manage distributed data storage solutions.
Work closely with data governance teams to ensure that data services meet all compliance, privacy and security standards (eg. GDPR, CCPA).
Implement policies and best practices for data management, lineage and quality.
Continuously monitor and optimize data processing performance, including optimizing data storage, query performance and the scalability of data pipelines.
Collaborate with product managers, business stakeholders and other engineering teams to understand data needs and provide high quality data services.
Foster a culture of continuous learning and best practices.
Create and maintain documentation for data services, pipelines and architectures.
Establish and promote best practices for data engineering, automation and scalability across the team.