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About
Responsibilities
Analyze data engineering problems and develop, build, and manage large-scale data structures, pipelines, and efficient ETL workflows.
Develop large-scale data structures and pipelines to organize, collect, and standardize data to generate insights and address reporting needs.
Write ETL (Extract/Transform/Load) processes, design database systems, and develop tools for real‑time and offline analytic processing.
Collaborate with Data Science team to transform data and integrate algorithms and models into automated processes.
Test and maintain systems and troubleshoot malfunctions.
Leverage knowledge of Google Cloud Platform tools to design and optimize queries for data pipelines.
Use programming skills in Python, Java, or similar languages to build robust data pipelines and dynamic systems.
Build data marts and data models to support Data Science and other internal customers.
Integrate data from various sources and ensure adherence to data quality and accessibility standards.
Analyze current IT environments to identify and assess critical capabilities and recommend solutions to complex business problems.
Experiment with available tools and advise on new tools to provide optimal solutions for model/use case requirements.
Requirements
Master’s degree (or foreign equivalent) in Computer Science, Data Science, Electrical Engineering, Computer Information Systems, or a related field, plus completion of a university‑level course, research project, internship, or thesis in each of the following:
GIT; Java, Python, or Node.js; XML, JSON, HTML, CSS, or JavaScript.
SAFe Software Development Principles; Amazon Web Services (AWS) and Google Cloud Platform (GCP).
Machine learning, statistical analysis, and predictive modeling.
NLP (Scikit‑Learn, SpaCy, PyTorch, or Spark NLP).
Contributing to large‑scale applications development, data science, or data analytics projects.
Quantitative analysis techniques, including clustering, regression, and pattern recognition.
Feature engineering, model training, hyperparameter tuning, distributed model training, and supervised and unsupervised learning implementation.
Additional Info
Pay range: $118,102 – $144,200 per year.
Telecommuting available; multiple positions.
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Languages
- English
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