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Analytics Engineer, Corporate Vice President

New York Life Insurance Company LogoNew York Life Insurance Company
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Salary

Salary range: $105,000-$160,000

The Center for Data Science and Artificial Intelligence (CDSAi) is the 60-person innovative corporate Data Science group within New York Life, led by Chief Analytics Officer Glenn Hofmann. We are a rapidly growing entrepreneurial department which designs, creates, and deploys innovative data-driven solutions for many parts of the enterprise. For more opportunities in data science, please visit our website.

You will join a team of analytics engineers, to provide clean transformed data for data science solutions. You will build data transformation pipelines which adhere to software and data engineering best-practice standards. You'll be an instrumental member of the Analytics Engineering team, part of the wider MLOps umbrella, led by Boris Simanovich.

Responsibilities:

  1. Partner with data scientists to help explore, analyze, and source data from our strategic sources.
  2. Create data pipelines to provide clean transformed data for data science/analytics use cases.
  3. Maintain best practices to coding formats, naming conventions, and version control.
  4. Review code changes and approve pull requests (PRs) in Git
  5. Convert requirements into Jira stories and provide milestones.
  6. Partner with data scientists regarding data quality, availability and value of data
  7. Collaborate with data stewards throughout NYL
  8. Build strong relationships with Technology (IT) and business stakeholders.
  9. Effectively articulate information and ideas to a diverse group of people
  10. Stay up to date with the latest Analytics Engineering trends/emerging technologies and look for opportunities to improve the stack.

Required qualifications:

  • 7+ years of industry experience in data analytics, business intelligence and/or data engineering
  • Expert in advanced SQL and Python (using dbt or similar tools)
  • Experience with building data models
  • Experience in automating and managing SQL using software engineering best practices
  • Have previously developed standards and practices for data/analytics engineering (architectural data pipeline patterns)
  • Experience with big data platforms such as Redshift, Snowflake and Hadoop
  • Excellent command of Git
  • Experience with cloud compute environments (AWS) along with cloud-native tools
  • Experience with Agile/Scrum methodology and best practices.
  • Graduate-level degree in computer science, engineering, or relevant work experience

Preferred:

  • Previous work experience with Data Science/AI teams
  • Exposure to Generative AI and use of unstructured data
  • Insurance industry experience

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