Data Engineer

Overview

We are an innovative company looking for a skilled Middle Data Engineer to join our dynamic team. Our collaborative culture fosters creativity and leverages the latest technologies. As a key member of our team, you will design, build, and maintain the data infrastructure and pipelines necessary for efficient and reliable data processing. You will ensure the smooth flow of data from various sources to the desired destinations, enabling data-driven decision-making across the organization.

If you are passionate about data and have a knack for optimizing data processes, we would love to hear from you.

Salary: $153,142 - $204,708 per year
Location: TX, New Braunfels, US, 78132
Employment option: Full time

• Collaborate with stakeholders to design and implement effective data architecture solutions that meet business requirements and support scalability.
• Build efficient and robust Extract, Transform, Load (ETL) processes to extract data from various sources, transform it into a usable format, and load it into data warehouses or data lakes.
• Ensure data security and privacy.
• Design and develop data pipelines to facilitate the seamless flow of data between systems, ensuring data availability and integrity.
• Implement data quality checks and validation processes to ensure data accuracy, consistency, and integrity.
• Identify and implement optimizations to improve data processing performance.
• Document data engineering processes, data flows, and system architecture.
• Stay up-to-date with the latest data engineering trends and technologies.

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Engineering, or a related field.
• B2+ English level.
• Experience with data warehousing concepts and technologies, such as Amazon Redshift or Google Big Query.
• Experience with cloud platforms like Amazon Web Services (AWS) or Azure, and their data engineering services.
• 3+ years of experience in data engineering or a related role.
• Experience with data integration and ETL tools.
• Experience with big data technologies such as Hadoop and Spark.
• Proficiency in designing and developing ETL processes and data integration solutions using tools like Apache Spark or Python libraries such as pandas.
• Strong knowledge of relational databases and SQL for data manipulation, query optimization, and performance tuning.
• Proficiency in programming languages commonly used in data engineering, such as Python or Java.
• Understanding of machine learning and data science concepts.
• Understanding of data security and privacy best practices.
• Familiarity with data pipeline orchestration tools.
• Strong analytical and problem-solving skills to identify and resolve data engineering challenges.
• Effective communication skills and the ability to collaborate with cross-functional teams to understand requirements and deliver data solutions.