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Summary:
- The Senior Analytics Engineer will join the Personalization Surfaces Data Science team supporting personalization and insights across user-facing surfaces, including Home, Search, and Promotions platforms.
- These surfaces are critical entry points for hundreds of millions of users, and the role directly influences user engagement with personalized content.
- The position is focused on leading the data and pipeline strategy to ensure data scientists have reliable and efficient access to datasets and metrics.
- Collaboration with data engineering and platform teams will improve data quality, streamline workflows, and scale best practices for data management.
- The candidate will be responsible for designing and implementing scalable data pipelines and enabling the efficient use of internal and external datasets.
- The Senior Analytics Engineer will play a pivotal role in delivering faster, deeper insights to shape the user experience on key product surfaces.
Responsibilities:
- Develop and implement a data and pipeline strategy that ensures scalable, reliable access to high-quality datasets and metrics.
- Design and, with the technically skilled data science team, build and maintain efficient data pipelines and models using tools like dbt and Google Cloud.
- Collaborate with data engineering teams and platform partners to enhance data usability, reliability, and scalability for key business use cases.
- Partner with data scientists to develop robust metrics and implement automated metrics monitoring via dashboards or other tools.
- Lead the implementation of data management benchmarks, reducing time spent on data wrangling and enabling data scientists to focus on impactful analyses.
- Streamline access to critical datasets and metrics that power personalization and recommendations on key product surfaces.
- Identify and implement opportunities to improve workflows and eliminate redundant or manual data management tasks.
- Act as a data strategist, empowering the Data Science team to better use internal and external data assets.
Requirements:
- Extensive experience in analytics engineering, data engineering, data science, or a related field, with a focus on enabling data-driven decision-making.
- Strong expertise with dbt (or similar), proficiency in SQL, data modeling, and building/managing scalable data pipelines in cloud-based environments (e.g., Google Cloud Platform, AWS).
- Proven track record of working cross-functionally with data scientists, data engineers, and product teams to enhance data workflows and usability.
- Strategic problem solver who can identify inefficiencies in data processes and implement solutions with measurable impact.
- Experience mentoring and guiding teams on best practices for data management and utilization.
- Excellent collaboration and communication skills, with the ability to influence and align teams across different organizational levels.
