Data Engineer

HappyGlobal Service; California; On-site; USD 120,000 - 180,000; Full time

Job summary

HappyGlobal Service is seeking an experienced Data Engineer to design, develop, and maintain scalable data warehouse solutions and processing pipelines. You will collaborate with cross-functional teams to deliver reliable data assets that drive data-driven decisions.

The ideal candidate combines strong technical skills with business understanding and can translate complex requirements into scalable data architectures. Strong knowledge of big data tech and modern data modeling is required.

Qualifications

• Bachelor's degree in a related field or equivalent professional experience.
• Hands-on data warehouse modeling and development experience.
• Experience with batch and real-time data processing.
• Experience with large-scale, high-throughput data systems preferred.
• Proficiency in Hadoop/Spark/Iceberg/Flink; Java/Scala/Python proficiency.

Responsibilities

• Design, develop, and maintain offline data warehouses and pipelines.
• Establish data modeling standards and data quality practices.
• Optimize architecture for scalability, performance, and maintainability.
• Collaborate with cross-functional teams to meet analytics needs.
• Implement data quality checks and governance practices.

Skills

Data modeling

Batch & real-time processing

Data warehouse development

Analytical thinking

Education

Bachelor's degree in Computer Science / Software Engineering / Data Engineering (or equivalent experience)

Tools

Hadoop

Spark

Iceberg

Flink

Java

Scala

Python

Job description

We are seeking an experienced Data Engineer to design, develop, and maintain scalable data warehouse solutions and data processing pipelines.

In this role, you will drive data warehouse architecture, data modeling, pipeline development, and data quality initiatives. You will collaborate with cross-functional engineering teams to deliver reliable, scalable, and business-oriented data solutions that support data-driven decision-making.

The ideal candidate combines strong technical expertise in big data technologies with solid business understanding and the ability to translate complex requirements into efficient data architecture.

Key Responsibilities

1. Data Warehouse Development

• Design, develop, and maintain offline data warehouses, including layered data models and data processing pipelines.
• Establish data modeling standards and data quality practices to improve accuracy, consistency, and reliability.
• Optimize data warehouse architecture for scalability, performance, and long-term maintainability.
• Monitor and improve pipeline performance and operational stability.

2. Business-Oriented Data Solutions

• Develop a strong understanding of business requirements across different functional areas.
• Design domain-specific data warehouse models and solutions to support diverse analytical and reporting needs.
• Translate business requirements into scalable data architectures and reusable data assets.
• Work with stakeholders to improve data accessibility, consistency, and usability.
• Develop and optimize batch and real-time data processing pipelines.
• Build reliable data processing solutions for large-scale, high-volume datasets.
• Evaluate and integrate appropriate technologies to improve data processing efficiency.
• Troubleshoot data pipeline issues and implement long-term improvements.

4. Data Quality & Governance

• Implement data quality checks, validation processes, and monitoring mechanisms.
• Establish data governance practices to support data consistency, traceability, and reliability.
• Collaborate with relevant teams to maintain appropriate data management and access practices.
• Support data engineering operations and continuous process improvement.

5. Cross-Functional Collaboration

• Collaborate with distributed data engineering teams on technical design, development, deployment, and maintenance.
• Coordinate engineering tasks and support effective knowledge sharing across teams.
• Follow applicable data management, privacy, and security requirements when developing and operating data solutions.
• Contribute to engineering standards and best practices across the data platform.

Qualifications

• Bachelor's degree in Computer Science, Software Engineering, Data Engineering, or a related field preferred. Equivalent professional experience will also be considered.
• Strong hands-on experience in data warehouse modeling and development.
• Practical experience with both batch and real-time data processing.
• Experience building and optimizing large-scale, high-throughput data processing systems is highly preferred.
• Strong knowledge of big data technologies, including Hadoop, Spark, Iceberg, and Flink.
• Proficiency in one or more programming languages, such as Java, Scala, or Python.
• Solid understanding of data modeling, data architecture, pipeline optimization, and data quality management.
• Ability to translate complex business requirements into scalable and maintainable data solutions.
• Strong analytical thinking, problem-solving, and logical reasoning skills.
• Ability to manage multiple priorities in a fast-paced engineering environment.
• Strong sense of ownership, initiative, and accountability.
• Familiarity with data privacy and data protection requirements applicable to data engineering is a plus.
• Professional working proficiency in English and the ability to collaborate effectively with distributed teams.

We are committed to providing equal employment opportunities and consider qualified applicants based on their skills, experience, and job-related qualifications, in accordance with applicable federal, state, and local laws.