Lead Python Data Engineer
Dice
Job Description
We are currently seeking an experienced Python Data Engineer to join the Big Data and Advanced Analytics department. As part of the Data Engineering team, the Lead Python Data Engineer will work closely with Business domain experts and Data Scientists to solve real-world oil and gas midstream problems using advanced analytics, machine learning, and artificial intelligence. This individual will provide analytical and technical leadership to the team to advance the data engineering practice within the organization.
Responsibilities include:
- Work directly with Business domain experts and Data Scientists to develop high quality, reliable, scalable, machine learning systems.
- Design and implement frameworks and tools to streamline the machine learning process.
- Automate manual data collection and processing tasks to improve efficiency.
- Leverage software architecture and design patterns to develop fault tolerant microservices.
- Convert research-based machine learning models into production-ready software.
- Implement processes to ensure coding standards, code quality, documentation, and test coverage.
Qualifications
- 7+ years of programming experience in Python
- Expertise in developing and maintaining data pipelines.
- Experience in testing, packaging, and deploying machine learning models.
- Experience in software engineering practices such as Design Principles and Patterns, Unit Testing, Refactoring, CI/CD, and version control.
- Expertise in Object-Oriented Design Principals and Functional Programming Principals.
- Experience with common Python Data Engineering packages including Pandas, Numpy, Pyarrow, Pytest, Scikit-Learn, and Boto3.
- Experience in storage technologies including SQL relational databases and Object Storage such as AWS S3.
- Experience in implementing distributed computing systems.
- Experience in designing modular, reusable software components.
- Experience in developing API endpoints and microservices.
- Knowledgeable of MLOps Principles.
Knowledgeable of ML platform technologies
- Apache Airflow
- Kubernetes
- Dask
- Ray
- MLFlow
Expertise level
Work arrangement
Key skills
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