Data Engineer, gTech Users and Products Engineering

Minimum qualifications

• Bachelor’s degree or equivalent practical experience.
• 1 year of experience coding in one or more programming languages.
• 1 year of experience designing data pipelines and dimensional data modeling for synchronous and asynchronous system integration and implementation using internal (e.g., Flume) and external stacks (DataFlow, Spark).
• Experience working with data models by performing exploratory queries and scripts.

Preferred qualifications

• Experience partnering with stakeholders, including users, partners, and customers, and managing stakeholders/customers.
• Experience developing project plans and delivering projects on time within budget and scope.
• Experience designing data models, data warehouses, and modeling complex business processes or real-world business data.
• Experience writing and maintaining ETL pipelines for structured and unstructured sources, alongside large-scale distributed data processing.
• Proficiency with Unix or Linux environments and non-relational data storage systems, including NoSQL and distributed databases.
• Excellent written communication, organizational, and investigative skills.

About the job

The role supports users and products by creating helpful, trusted experiences across the product ecosystem. It involves meeting partners and consumers where they are with support and help, representing their needs to product partners, and proposing fixes and features that improve engagement. The team also provides product services such as localization, digitization, and partner integration, helping optimize products for users worldwide.

Responsibilities

• Leverage advanced AI to design, develop, and support robust, full-stack data pipelines, warehouses, and reporting systems.
• Create, optimize, and modify scalable ETL processes using traditional and large-scale distributed data systems to manage evolving business demands.
• Partner closely with data scientists to transition, scale, and productionize statistical and machine-learning models within data processing pipelines.
• Align and collaborate with product, user, and engineering stakeholders to ensure data infrastructure meets dynamically evolving operational requirements.
• Author comprehensive technical design documents while managing continuous innovation using investigative tools for business insights.