Data Engineer I: Build Reliable Data Pipelines

Daikin Comfort; Waller (TX); On-site; USD 85,000 - 120,000; Full time

Job summary

Daikin Comfort is seeking a Data Engineer to build a modern, secure data environment that connects data across the company and treats data as an asset. You will support software developers, data analysts and data scientists to ensure reliable data delivery and quality.

Responsibilities include automating processes, designing scalable data pipelines using SQL and AWS big data technologies, collaborating with stakeholders across departments, and improving data quality and efficiency through

Qualifications

• Bachelor’s degree in Engineering, Data Science, Computer Science or equivalent with formal training/certifications.
• Master’s degree preferred.

Responsibilities

• Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, redesigning infrastructure for scalability.
• Build infrastructure for optimal ETL of data from diverse sources using SQL and AWS big data technologies.
• Collaborate with Executives, Manufacturing, Sales and Marketing to support data infrastructure needs.
• Work with data/analytics experts to improve functionality of data systems.
• Develop ways to improve data quality, reliability and efficiency.
• Perform additional projects to support ongoing business needs.

Skills

Programming knowledge

Analytical skills

Communication

Time management

Collaboration

Independent work

Judgment

Education

Bachelor's degree in Engineering/Data Science/CS

Master's degree preferred

Tools

SQL

AWS Big Data

Job description

Daikin Comfort is seeking a Data Engineer to build a modern, secure data environment that connects data across the company and treats data as an asset. You will support software developers, data analysts and data scientists to ensure reliable data delivery and quality.

Responsibilities include automating processes, designing scalable data pipelines using SQL and AWS big data technologies, collaborating with stakeholders across departments, and improving data quality and efficiency through