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Career Accelerator Program - Data Engineer
Texas Instruments
Richardson, USA
Full-time
Entry
Onsite
Discovered Today
PythonJavaScalaSQLApache SparkApache Kafka
Free
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PythonJavaScala
Role Overview
Designs, builds and oversees the deployment and operation of technology architecture, solutions and software to capture, manage, store and utilize structured and unstructured data from internal and external sources.
Establishes and builds processes and structures based on business and technical requirements to channel data from multiple inputs, route appropriately and store using any combination of distributed (cloud) structures, local databases, and other applicable storage forms as required.
Develops technical tools and programming that leverage artificial intelligence, machine learning and big-data techniques to cleanse, organize and transform data and to maintain, defend and update data structures and integrity on an automated basis.
Creates and establishes design standards and assurance processes for software, systems and applications development to ensure compatibility and operability of data connections, flows and storage requirements.
Reviews internal and external business and product requirements for data operations and activity and suggests changes and upgrades to systems and storage to accommodate ongoing needs.
Key Skills for This Role
PythonJavaScalaSQLApache SparkApache Kafka
Full Job Posting
Qualifications
- Minimum Requirements:
- Bachelor's degree in Electrical Engineering, Computer Engineering or related field of study
- Minimum cumulative GPA 3.0/4.0
- Preferred Qualifications:
- Proficiency in at least one programming language commonly used in data engineering (Python, Java, or Scala)
- Solid understanding of SQL and relational database concepts
- Coursework or project experience involving data structures, algorithms, and software design fundamentals
- Strong analytical and problem-solving skills, with the ability to learn new tools and technologies quickly in a fast-paced environment
- Exposure to distributed data processing frameworks such as Apache Spark
- Familiarity with streaming/event-driven architectures (e.g., Apache Kafka)
- Understanding of modern data lake/lakehouse concepts, including table formats like Apache Iceberg and object storage systems (e.g., MinIO, S3)
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