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Staff Analytics Engineer, Ads

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Salary

The base pay range for this position is: $206,700 - $289,400 USD

Location

Remote

Reddit is poised to rapidly innovate and grow like no other time in its history and the Ads team fuels that growth. As an Analytics Engineering lead on the Ads Data Science team, you will achieve a Reddit-wide impact by leading a team of full-stack data scientists and performing hands-on execution. You will create a first-class Ads data warehouse and data tools to provide scalable solutions that meet a wide range of evolving needs - including high-quality metric reporting, product insights, and data engineering for ML models. You will play a critical role in making ads data more accessible across reddit - unlocking innovation through the self-service of our data from Engineering to Sales teams. If you are passionate about building high-quality data products and leading a strategy to create an agile but reliable foundation to accelerate our advertising business, Reddit will be the perfect home for you.

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.

Responsibilities:

  • Act as the analytics engineering lead within Ads DS team and a key contributor to the success of data science data quality and automation initiatives.
  • You will have a keen interest in the collection and quality of underlying data (experiment design and analysis, data deep dive) and in working on ETLs, reporting dashboards, and data aggregations needed for business tracking and/or ML model development.
  • 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. Driver widespread adoption of these tools and applications
  • Lead transformational efforts to build a data-driven culture at Reddit by enabling data self-service.
  • Provide technical guidance, mentorship, coaching and/or training to data analysts
  • Serve as a thought partner for data scientists, engineering managers, and leadership on data foundations, communicating and shaping the data foundations roadmap and strategy for Reddit

Required Qualifications:

  • MS or PhD in a quantitative discipline: engineering, statistics, operations research, computer science, informatics, applied mathematics, economics, etc.
  • 7+ years of experience working with large-scale ETL systems (implementation, strategy, and maintenance), building clean, maintainable, object-oriented code (Python preferred) in a production environment.
  • Strong programming proficiency in Python, SQL, Spark, Scala, etc.
  • Experience with data modeling, ETL (Extraction, Transformation & Load) concepts, and patterns for efficient data governance. Experience with manipulating massive-scale structured and unstructured data.
  • Experience with data workflows (such as Airflow), data modeling, front-end or back-end engineering.
  • Experience in data visualization and dashboard design, including tools such as Looker, Tableau, R visualization packages, streamlit, D3, and other libraries, etc.
  • Deep understanding of technical and functional designs for relational and MPP Databases
  • Proven track record of cross-functional execution and collaboration. Excellent communication skills to collaborate with cross-functional stakeholders at all levels of the company.
  • Experience in mentoring junior data scientists and analytics engineers.
  • Self starter, ability to work independently and autonomously, as well as part of a team.

Nice to have:

  • Ads domain experience, including metrics, tracking, and ads product understanding, is a big plus
  • Past experience collaborating closely with data scientists, machine learning engineers, and product managers.

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