Senior Analytics Engineer

Reddit has a flexible workforce! If you happen to live close to one of our physical office locations, our doors are open so you can come into the office as often as you'd like. Don't live near one of our offices? No worries: You can apply to work remotely in any country in which we have a physical presence

We are looking for a talented and driven individual to be a key part of our Analytics Engineering team within the Data Science organization, focused on the Sales and Marketing domain. We are looking for someone who can work closely with Data Scientists and members of Sales and Marketing cross-functional teams to curate, develop, and deploy the right data and analytic tooling to drive Reddit’s business forward and provide a data and tooling foundation that will last decades. Your work will empower thousands of your colleagues to grow our Sales and Marketing reach.

Successful candidates have a strong track record of understanding and deeply caring about the purpose of data to support business goals, and can act as an effective conduit between Data Producers and Data Consumers. This role sits at the intersection of Data Science and Data Engineering, and the ideal candidate has skills, experience, and passion in both areas. 

Responsibilities:

• Be the Analytics Engineering lead within the Sales and Marketing organization and a key contributor to the success of Data Science data quality, performance, and automation initiatives. 
• Be the data steward for Sales and Marketing: architect and improve the collection of underlying data while also creating ETLs, reporting dashboards, data aggregations and other deliverables needed for business tracking, advertiser outreach and acquisition, marketing campaigns, and other data-driven activities. 
• Develop and maintain robust data pipelines and workflows for data ingestion, processing, and transformation. Work closely with engineering to ensure the quality and reliability of these data pipelines. 
• Create user-friendly tools and applications for internal use across Data Science and cross-functional teams, streamlining data analysis and reporting processes. Drive widespread adoption of these tools and applications with a relentless focus on automation, consistency, and reliability. 
• Lead transformational efforts to buil