Data Analyst II

Job Description

Product Operations' focus is to support and educate the people and businesses who use our products through direct interactions and scalable solutions. We develop processes to help improve the health and quality of our products and work with cross-functional partners to ensure a high-quality experience on our platforms. You will gain experience and make an impact in a fast-growing organization. Those who join our teams are very passionate about solving user issues and are strong advocates for users globally. If you like helping people, Product Operations is for you.

The Product Operations team ensures that the suite of applications and platforms are as reliable as possible by working closely with Global Operations, Data Science, Engineering, Product, and several other teams.

We are looking for a Data Analytics Engineer who shares our passion for helping make applications and products reliable and safe. The right candidate will be comfortable in a fast-moving organization and excited to collaborate cross-functionally. You will guide teams by building optimal data artifacts (including datasets and visualizations) to address key questions. You will refine systems and create scalable data models. Ensuring data security and quality, and with a focus on efficiency, you will suggest architecture and development approaches and data management standards to address complex analytical problems. You will possess strong quantitative analytical skills, statistical knowledge, data visualization skills, and communication skills, along with the ability to build strong partnerships to drive impact across the organization.

Responsibilities

Partner with teams within Product Operations, Global Operations, Data Science, Data Engineering, Product, and Engineering to solve problems and identify trends and opportunities

Build and maintain data infrastructure (reporting layer data pipelines, reports, dashboards, alerts) to monitor the performance of operations and drive business understanding

Optimize pipelines, dashboards, frameworks, and systems to facilitate easier development of data artifacts

Assist in owning existing processes running in production, optimizing complex code through advanced algorithmic concepts

Design, build, and launch collections of sophisticated data models and visualizations that support multiple use cases across different products or domains

Independently analyze data, conduct research, and synthesize feedback into plans, processes, and playbooks

Proactively propose creative technical and quantitative solutions to problems and drive these through to implementation by identifying data and tooling requirements that enable self-service and scalable solutions

Communicate results of analyses to non-technical stakeholders who are the users of systems involving metrics, pipelines, and dashboards

Define metrics and KPIs for end-to-end product operations and build repeatable and reproducible analyses

Utilize AI tools to help automate routine analyses and scale solutions

Minimum Qualifications

Bachelor's Degree in a technical or research-oriented field such as engineering, data science, social science, or related fields, or equivalent practical experience

Experience in strategy, operations, consulting, statistics, data analysis, data science, or directly related fields

Experience with ETL pipeline development

Proficiency with intermediate to advanced SQL concepts for data extraction

Experience managing multiple projects and meeting deadlines in a fast-paced environment

Experience creating dashboards with Tableau, Power BI, Alteryx, or other data visualization tools

Experience with statistical analysis, including hypothesis testing, regression, and experimental design

Experience communicating and presenting findings to non-technical stakeholders

Preferred Qualifications

Advanced technical or graduate degree in statistics, marketing, or related fields

Experience using AI for data analysis and summarization

Experience measuring the performance of AI models

Experience working across time zones and with diverse audiences, including within operations teams and with key stakeholders and senior leadership