Ring Data Science and Engineering team is looking for a Data Engineer (DE) to play a significant role in building their large-scale, high-volume data services. This role will be contributing to building extendable systems to support a variety of data needs, from handling large volumes of device data needed for troubleshooting and analytics, to organizing data for usage in AI platforms to provide analytics with agility.

Key job responsibilities

This role will be responsible for building and maintaining efficient, scalable, and privacy/security compliant data pipelines to support our business stakeholders. These pipelines will be built using both tools available in Native AWS as well as Amazon internal tools and technologies.

This role will need to be able to work closely with stakeholders to understand their needs and work alongside them to ensure data being ingested meets the business user´s needs and will be well modeled and organized to promote scalable usage and good data hygiene.

This role is expected to leverage new technologies like Gen AI and adapt to business needs, building and improving frameworks and processes with these new tools to accelerate development and improve data quality.

A day in the life

This Role will:

- Collect and Discuss requirements from Business Stakeholders across verticals such as Subscriptions, Sales, Reverse Logistics, Finance, Product, etc.

- Build new data ingestions using a combination of Native AWS services and/or internal Amazon tools.

- Maintaining/Improving existing data ingestions and ensuring they meet evolving standards and corporate mandates.

- Building/Improving/Maintaining frameworks or tools for internal team and external stakeholder usage to manage data.

- Performing Code Reviews and ensuring best practices are followed for ETL and Data Hygiene by team members and supporting teams.

- Building and updating AI Agents to streamline Data Engineering Tasks

BASIC QUALIFICATIONS

- Experience in data engineering

- Experience with data modeling, warehousing and building ETL pipelines

- Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)

- Experience with one or more scripting language (e.g., Python, KornShell)

PREFERRED QUALIFICATIONS

- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR

- Experience with any ETL tool like, Informatica, ODI, SSIS, BODI, Datastage, etc.