Senior Analytics Engineer

As a Senior Analytics Engineer, you will be responsible for the design, development and monitoring of data products (especially feature store), packages and processes that will help streamline the creation and deployment of data science solutions made by our Data Scientists.

NATURE OF WORK

• Work with our Data Scientists to design datasets that are useful for creating statistical and machine-learning models
• Design, develop and maintain feature stores as well as the accompanying feature pipelines that will be used in creating training data as well as real-time inference features.
• Implement data quality and integrity checks and ensure the quality and availability of data sources in accordance with their SLAs.
• Align with Data Engineering and Data Governance team to achieve maturity in the data.
• Create and maintain software packages for use by our Data Scientists to help improve their model development workflow.
• Build CI/CD pipelines and microservices, to improve time to deployment and proactively catch issues before they hit production
• Provide guidance on best practices for code and architecture of data pipelines and microservices, and do code and architecture reviews to ensure adherence to best practices
• Communicate technical architecture and solutions, as well as explain the competitive advantage of various technologies to a broad audience
• Create and maintain architecture and systems documentation

NICE TO HAVE

• High proficiency in Data Warehouses (Redshift, Databricks, etc) and manipulating data within them (using SQL or Spark).
• High proficiency in the design, development, and monitoring of ETL pipelines
• Moderate experience (at least 2 years) in working with AWS or any Cloud providers (such as GCP or Azure).
• Moderate experience in creating and evangelizing best practices and tools
• Moderate experience in interacting with different stakeholders at different levels.
• Some experience (at least 1 year) with common data science tools, packages (Pandas, SKLearn), and concepts
• Good programming skills (Python, R, Bash scripting, or any languages for ETL pipelines)
• Moderate Experience working in an Agile, Dev Ops, Test Driven Development environment
• Experience in designing, developing, and optimizing ML Feature Store is a plus.
• Experience in working with Sagemaker is a plus.
• Experience in building CI/CD pipelines and data testing for data integrity and correctness is a plus.
• Experience with building streaming applications using Kafka, Kinesis, or other message queues is a plus.
• Experience with using Data Build Tool (DBT) for ETL is a plus

REQUIRED QUALIFICATIONS

• With at least a bachelors degree in any quantitative discipline (i.e. Computer Science, Math, Physics, etc)
• Having at least 5 years of experience in creating building and maintaining ETL pipelines
• Having at least 1 year of experience in managing stakeholders