What you’ll do:

This dynamic role is responsible for translating complex business requirements into valuable data designs, as well as implementing and delivering the required engineering underpinnings to support the analytical and data-driven nature of the enterprise.  The role will be tasked with gleaning insights from the inherent data and building complex translations to address current business challenges and future desires.

Who you are:

• Hands-on developer and teammate with passion for working on a variety of data projects.
• Collaborates directly and maintains flexibility with business to meet desired outcomes.
• 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 best practices are met and deployed within the context of the data teams standards.
• Conducts code reviews, provides advice, and gives constructive feedback to fellow analytical engineers.
• Creates 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 aims to enhance the team around you. 
• Passion for leaving the code base better than it was found.

Preferred Qualifications: 

• Bachelor's degree in Computer Science, MIS, mathematics, business, or an equivalent combination of education and experience.
• Maintains a strong command of ANSI SQL, and Python experience is a plus.
• Experience working with cloud data platforms, e.g. GCP Big Query, Azure Synapse, or AWS Redshift.
• Experience working with data integration tools, e.g. such as Fivetran, dbt, or Matillion.
• Experienced across all phases of data modeling, from conceptual designs to physical designs.
• Understanding of modern data architecture and engineering concepts such as streaming, schema-on-read, and real-time data access.
• Ability to assess business needs and translate them into appropriate data designs.
• Experience researching performance bottlenecks, diagnosing problems, and articulating viable solutions to execute.
• Knowledge of how to assess the performance of data solutions, how to diagnose performance problems, and tools used to monitor and tune performance.
• General understanding of distributed version control systems, e.g. Git.
• Desire to work in a rapidly changing environment and maintain the ability to pivot in new directions as business needs are altered.
• Ability to lead, manage, coach, mentor, and empower team members on a global scale.