About the role

This key architecture role is responsible for translating sophisticated business requirements into valuable coordinated data models and maintaining a clean and optimized ELT code base. The role will be tasked with working within a multi-functional team to support the development of an enterprise data warehouse.

Who you are:

• Hands-on developer and teammate with a passion for working on a variety of data projects.
• Supports colleagues in building strong functional requirements and provides insight as a technical liaison with respect to delivery options and opportunities.
• Articulates advantages and disadvantages of data design approaches and modeling methodologies.
• Maintains metadata catalog in an effort to support a scalable self-service analytics environment.
• Ensures that technical requirements and standard methodologies are met and deployed within the context of the data team’s standards.
• Conducts code reviews, provide advice, and gives constructive feedback to analytical engineers and developers.
• Builds open dialogues and discourses with data team leads to remove much of the opacity in directional decision making.
• Supports the drive for innovation through identifying emerging trends and their alignment to business outcomes.
• Builds trust in all interactions and always strives to improve the team around you.
• Exudes attention to detail, and a passion for leaving a code base better than it was found.

Requirements:

• Bachelor's degree in Computer Science, MIS, mathematics, business, or an equivalent combination of education and experience.
• Experience working with cloud data platforms, e.g. GCP Big Query, Azure Synapse, or AWS Redshift.
• Mastery across all phases of data modeling, from conceptual designs to physical designs, with a deep understanding of different modeling methodologies, e.g. dimensional modeling.
• Maintains a proven command of ANSI SQL. Python experience is a plus.
• Experience working with dbt, including authoring sophisticated Jinja and advanced dbt functionality.
• Understanding of modern data architecture and engineering concepts such as streaming, schema-on-read, and real-time data access.
• Extensive experience in how to assess the performance of data solutions, how to diagnose performance problems, and tools used to supervise and tune performance.
• Desire to work in a constantly evolving environment and maintain the ability to shift in new directions as business needs are altered.
• Deep understanding of distributed version control systems, e.g. GitHub, and history of retaining ownership of the cleanliness of multiple repositories.
• Basic understanding of a DataOps approach to delivery.

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