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Who We Are:
The Marketing Insights & Analytics team is tasked with proving and improving the value of X as well as demonstrating the value of our audience to the marketplace. Our team seeks to understand how consumers think, feel, and behave on X. Through a combination of ad campaign measurement and market-facing thought leadership projects, we use data to uncover who our audiences are and how they react to advertising. Recent examples include demonstrating the brand impact value of our Amplify ad products as well as showing how X is the go-to social platform for Olympics content.
Key Qualifications:
- 5+ years in data architecture and engineering in generative AI or machine learning contexts.
- Proven experience designing scalable data models, architectures, and pipelines, with proficiency in cloud platforms (AWS, GCP, or Azure) and data warehousing solutions.
- Hands-on experience with data integration tools, ETL processes, and statistical analysis tools (e.g., SAS, SPSS, STATA, R, Matlab) or general programming.
- Familiarity with SQL for large-scale datasets; training provided if needed.
- Prior experience at an insights/analytics vendor, advertising agency, or ad sales research team is a plus.
- Skills:
- Strong expertise in designing data architectures that support generative AI applications, ensuring performance, scalability, and security.
- Proficiency in descriptive and inferential statistical techniques to derive insights from complex datasets.
- Exceptional leadership and collaboration skills to manage cross-functional teams and work with external partners/vendors.
- Client-responsive mindset with the ability to present complex concepts clearly to internal and external stakeholders within two months of hire.
- Strong organizational skills, attention to detail, and the ability to balance quick-turnaround projects with long-term strategic goals.
Key Responsibilities:
- Data Architecture Leadership:
- Design and implement robust data architectures to support generative AI capabilities, ensuring scalability, performance, and compliance with data governance and security standards.
- Develop and maintain data pipelines, ETL processes, and integration tools to enable seamless data flow for AI-driven initiatives.
- Collaborate with data scientists, engineers, and business stakeholders to define data strategies and roadmaps aligned with business objectives.
- Evaluate and integrate new technologies and tools to enhance data architecture capabilities.
- Team and Stakeholder Engagement:
- Provide technical guidance and mentorship to team members, fostering professional growth in data engineering and analytics.
- Engage with external partners and vendors to drive innovation and evaluate new tools.
- Promote a culture of data quality, governance, and continuous improvement across teams.
- Collaborate across geographies, building relationships with colleagues for knowledge sharing.
