Data Engineer
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Job description
A U.S.-based company that has made a name for itself as a private lender and provides residential real estate investors with loans for investment properties, is looking for an MLOps Engineer. The engineer will be responsible for ensuring that Data engineering methods and solutions are documented and shared with the appropriate parties. The company draws on technology to provide real estate investors with an easier, more dependable, and quicker way to take control of their real estate investments. The company has managed to secure $156mn+ in funding so far.
Job Responsibilities:
- Create and maintain feature stores, CI/CD solutions, and machine learning models
- To enable smooth data integration, work with cross-functional teams such as Data Engineering, Data Science, and DevOps
- Take part in code reviews to guarantee good code and adherence to best practices
- Implement automated solutions to track and improve data processing systems, ensuring their effective and efficient operation
- Identify opportunities for automation and process improvement in close collaboration with other teams
Job Requirements:
- Bachelor’s/Master’s degree in Engineering, Data Science, or Computer Science (or equivalent experience)
- At least 5+ years of relevant experience as a data engineer
- Demonstrable experience working with Python, SQL, and PySpark
- Prolific understanding of AWS, GCP principles, and Azure Databricks is desirable
- Extensive experience developing and testing ML models and MLOps
- Prior working knowledge of CI/CD pipeline development and maintenance using CircleCI, Jenkins, or BuildKite
- Comprehensive understanding of SQL and NoSQL databases
- In-depth knowledge of offline batch models and online real-time models in big production contexts
- Extensive knowledge of process orchestrators like Airflow
- Experience using big data tools like Spark and Hive
- Strong familiarity with program design ideas, algorithms, and data structures
- Superior problem-solving abilities and meticulousness
- Ability to operate in a fast-paced, collaborative setting
- Experience with containerization tools like Docker or Kubernetes
- Fluent in spoken and written English communication
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