Senior Data Engineer – Python, Snowflake
CompassX Group
AirflowAmazon RedshiftBigQueryCloudDockerETLLinuxPandasPySparkPythonSQL
Senior AI/Python Engineer
A.C.Coy Company
full-time
Posted on:
Location Type: Remote
Location: Remote • North Carolina • 🇺🇸 United States
Visit company websiteJob Level
Senior
Tech Stack
AWSAzureCloudDockerGoogle Cloud PlatformKubernetesPython
About the role
- Design and develop scalable solutions using AI tools and machine-learning models.
- Perform research and testing to develop machine learning algorithms and predictive models.
- Utilize big data computation and storage tools to create prototypes and datasets.
- Conduct model training and evaluation.
- Integrate, test, tune, and monitor solutions.
- Perform data science tasks such as data acquisition, data cleaning, and feature extraction.
- Develop and demonstrate proofs of concept (PoCs), independently or as part of a team.
- Create technical diagrams and documentation to illustrate PoC implementations and potential production deployments.
- Research and present to teammates the latest tools, packages, and capabilities being developed.
- Recommend relevant tools and packages for use in production environments.
- Work with governance committees to document and obtain approval for exploratory data science initiatives.
- Collaborate with architecture teams to identify potential automation solutions, including AI/ML applications.
- Partner with cross-functional teams to design and implement holistic AI/ML solutions.
Requirements
- A degree from an accredited College/University in Software Engineering, Computer Science, Business, or related discipline.
- 8+ years of IT-related experience.
- Practical Application of Core Python Concepts: Demonstrated experience not just with Python syntax but in building and deploying Python applications or scripts that address IT operational needs, automate processes, or manage data effectively.
- Data Engineering and Analysis Skills: Proven experience in data acquisition, cleaning, preprocessing, and transformation using Python tools and techniques to enable robust analysis of large-scale datasets.
- Implementing and Deploying Cloud Applications: Hands-on experience deploying Python applications in cloud production environments (e.g., AWS, Azure, GCP), including the use of containerization tools such as Docker or Kubernetes.
- Understanding of Software Engineering Best Practices: Experience applying software engineering principles, including version control (Git), writing clean and testable code, participating in code reviews, and working within CI/CD pipelines.
- Knowledge of Data Science Best Practices: Demonstrated understanding and implementation of data science workflows such as data pipelining, feature engineering, and machine learning model development.
- Familiarity with Cloud-based Data Science Services: Proficiency with managed AI/ML services offered by major cloud providers to streamline the development, deployment, and maintenance of data science applications.
- Ethical Practices and Security Knowledge: Awareness and application of ethical guidelines in data science, including mitigating bias, ensuring data privacy, and implementing secure coding practices in Python-based solutions.
- Ability to communicate effectively, both orally and in writing, with technical and non-technical team members.
- Must be able to obtain a Position of Public Trust Clearance.
- Be able to pass a drug screening, criminal history, and credit checks.
- Must be a US Citizen or have permanent residence status (Green Card).
- Must have lived in the United States for the past 5 years.
- Cannot have more than 6 months travel outside the United States within the last five years. Military Service excluded. (Exception does not include military family members.)
Benefits
- Occasional travel may be required to support business efforts
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
machine learningdata acquisitiondata cleaningfeature extractionPythoncloud applicationsdata pipeliningfeature engineeringAI toolspredictive models
Soft skills
effective communicationteam collaborationresearch skillspresentation skillsproblem-solvingdocumentationethical practicesorganizational skillsinterpersonal skillsadaptability
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