What are the responsibilities and job description for the Google Cloud Platform Data Engineer (Locals Only) position at VeridianTech?
Job Details
Job Title: Google Cloud Platform Data Engineer
Location: Mountain View, CA (Onsite)
Duration: 12 months
What you'll do:
- Develop and enhance Python frameworks and libraries to support data processing, quality, lineage, governance, analysis, and machine learning operations.
- Design, build, and maintain scalable and efficient data pipelines on Google Cloud Platform.
- Implement robust monitoring, logging, and alerting systems to ensure the reliability and stability of data infrastructure.
- Build scalable batch pipelines leveraging Big query, Dataflow and Airflow/Composer scheduler/executor framework on Google Cloud Platform
- Building data pipelines, leveraging Scala, Pub Sub, Akka, Dataflow on Google Cloud Platform
- Design our data models for optimal storage and retrieval and to meet machine learning modeling using technologies like Bigtable and Vertex Feature Store
- Contribute to shared Data Engineering tooling & standards to improve the productivity and quality of output for Data Engineers across the company
Minimum Basic Requirements:
- Python Expertise: Write and maintain Python frameworks and libraries to support data processing and integration tasks.
- Code Management: Use Git and GitHub for source control, code reviews, and version management.
- Google Cloud Platform Proficiency: Extensive experience working with Google Cloud Platform services (e.g., BigQuery, Cloud Dataflow, Pub/Sub, Cloud Storage).
- Python Mastery: Proficient in Python with experience in writing, maintaining, and optimizing data processing frameworks and libraries.
- Software Engineering: Strong understanding of software engineering best practices, including version control (Git), collaborative development (GitHub), code reviews, and CI/CD.
- Data Management: Deep knowledge of data modeling, ETL/ELT, and data warehousing concepts.
- Problem-Solving: Excellent problem-solving skills with the ability to tackle complex data engineering challenges.
- Communication: Strong communication skills, including the ability to explain complex technical details to non-technical stakeholders.
- Data Science Stack: Proficiency in data analysis and familiarity with tools such as Jupyter Notebook, pandas, NumPy, and other Python data analysis libraries.
- Frameworks/Tools: Familiarity with machine learning and data processing tools and frameworks such as TensorFlow, Apache Spark, and scikit-learn.
- Bachelor's or master's degree in computer science, Engineering, Computer Information Systems, Mathematics, Physics, or a related field or software development training program
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
