The Data Engineer III is responsible for building and maintaining robust data pipelines and models for the R2Net organization, overseeing a broad scope of ETL infrastructure and databases. By deploying expertise across many diverse systems, APIs, and platforms, this role plays a key part in enabling accurate and timely access to data across the organization, with an emphasis on simplifying and centralizing a complex ecosystem – and thereby providing trustworthy, analytic-ready resources for the various business units.

This role, by interfacing with key stakeholders in Engineering, Analytics, Operations, Finance, Marketing, and Customer Service, will work to build a data environment that is accurate, complete, timely, and dependable, and will serve as a trusted partner to key associates across the org. In achieving these goals, this role will extend beyond pipeline management – it will involve deep collaboration with business stakeholders, proactive engagement in data strategy, and a focus on driving measurable impact through data. This engineer is expected to bridge technical execution with business outcomes, owning long-term initiatives that drive top-line and bottom-line growth for R2Net as a whole.

Key Responsibilities:

• Design, implement, and maintain complex data pipelines, ensuring scalability and reliability using Airflow, dbt, Rivery, Python, and SQL, enabling robust ingestion and transformation of structured and semi-structured data.
• Serve as a strategic partner to business teams, working closely with stakeholders to translate high-level goals into data solutions that support forecasting, performance tracking, and optimization.
• Develop and maintain clean, well-documented data models in Snowflake and BigQuery that support analytics, reporting, and operational workflows and contribute to architecture decisions.
• Integrate data from a variety of internal and external sources, including Google Analytics and third-party APIs, to support full-funnel visibility across departments.
• Enable self-service analytics by ensuring data assets are discoverable and usable via tools such as Tableau, including thoughtful semantic layer design and performance tuning.
• Contribute to the development of robust monitoring and observability practices for data quality and pipeline health.
• Collaborate on architecture and design decisions, including cloud infrastructure and containerization using AWS. Pulumi and Docker.
• Maintain strong documentation and promote engineering standards that ensure transparency, maintainability, and reusability of data systems.