Data Engineer On Azure Platform

Job Description Summary

The Data Engineer will be responsible for building and maintaining data management processes for our cloud-based Investment Data Analytics Platform, a strategic asset that drives quantitative-driven research and investment decision-making. Hired staff will design and build data validations, transformations, normalizations, reports and extracts, and integration processes that deliver unstructured and structured content to our cloud-based Data Lake and SQL Warehouse. The staff will partner with business users, senior architects, and infrastructure engineers to form complete end-to end-solutions. They thrive in an evolving fast-paced environment, and bring a work style marked by high energy, flexibility, quick learning, and collaboration.

Job Skills and Abilities

• Mix of education and experience with SQL Server, SQL Server Integration Services, and Azure Data Services (Data Factory, Databricks)

• In-depth SQL, plus Python, R and/or similar analytical languages

• Data modeling and design understanding, including conceptual, logical, and physical modeling

• Understanding of DevOps and CI/CD practices and tools

• High energy and collaborative style; flexibility and willingness to take on a broad range of engineering and support assignments

• Track record and skill sets demonstrated on high-intensity, high-complexity projects

• Comfort and confidence interacting with business users

• Ability to use analytical skills to translate business ideas into technology solutions

• Experience in Agile delivery framework

• Experience working with distributed external resources and vendor teams (onshore and offshore)

• Clear communicator of technical details both verbally and in writing

Responsibilities

Engineering: 95%

• Design and build of data validations, transformations, normalizations, reports and extracts, and integration processes
• Design and implementation of schemas and access views
• Issue resolution
• Version control, release planning, and deployment preparation
• Contribution to logical and physical data models
• Creation and updates to project artifacts and tools such as task boards, problem lists, user stories, and technical documentation

Professional Development: 5%

• Examine & evaluate cloud-based tools
• Prototype solutions
• Attend trainings to sharpen technical and organizational skills

Education

Work Experience