Our Company:
At Teradata, we believe that people thrive when empowered with better information. Teradata Autonomous Knowledge Platform activates enterprise intelligence by unifying data, knowledge and business context to achieve tangible outcomes. With Teradata, organizations can provide agents with full context for impact when it matters. Our solution lets businesses connect and scale on premises, in the cloud, or through a hybrid approach. Teradata delivers real business value with AI.

Who You’ll Work With

The individual will report to the manager of the Go To Market Insights and Analytics team. The individual will primarily support the Marketing team but must work cross-functionally across Field Sales, Sales Ops, Product Management, Finance, and others. 

What You’ll Do

• Collect and analyze marketing data including web analytics, event data, attribution data, and campaign data through quantitative and qualitative mechanisms to inform strategic decisions
• Translate complex marketing and web analytics data into clear, actionable recommendations that improve marketing performance, customer experience, pipeline generation, and conversion outcomes.
• Develop and maintain scalable data pipelines and feature engineering workflows to support model development, with a focus on web analytics data sources (Celebrus, Google Analytics)
• Design and own multi-touch attribution (MTA) models to quantify marketing's contribution to pipeline and revenue including sourced vs. influenced deal analysis across GTM, Marketing, and Partner channels
• Apply data science techniques (e.g., statistical modeling, machine learning) to improve attribution methodology, identify high-performing segments, and predict future pipeline contribution from marketing activities
• Design and conduct experiments (A/B testing, hypothesis testing) to validate findings and drive data-driven decisions for marketing stakeholders
• Collaborate with the data warehouse team to provide strategy and assist with building new semantic views and onboarding new marketing data sources
• Partner with Web Operations, UX, Content, SEO, and Campaign teams to identify opportunities for improving website engagement, conversion, and user experience
• Analyze site search behavior and search performance data to identify customer intent, content gaps, and opportunities to improve website engagement and conversion performance.
• Develop collaborative relationships with key stakeholders, and serve as the primary analytics point of contact for marketing business stakeholders
• Drive critical projects and initiatives partnering with cross-functional teams across the organization to frame and break down complex marketing business problems into key components, design analyses to surface insights, and recommend practical solutions 

What Makes You a Qualified Candidate

• Bachelor’s degree in computer science, mathematics, engineering, or another STEM field
• Experience using Power BI or similar BI tools is required
• Hands-on experience with web analytics platforms such as Celebrus or Google Analytics required
• Experience with data science projects and AI projects required
• Proficiency in Python or R for data analysis, statistical modeling, and machine learning workflows
• Advanced SQL writing capabilities
• Demonstrated experience building or working with marketing attribution models (multi-touch attribution, pipeline attribution, or revenue attribution) required
• Understanding of digital customer journeys
• Ability to leverage AI-driven technologies and machine learning frameworks
• Strong background in data modeling and building scalable data architecture
• Experience working with data privacy regulations (e.g., GDPR, CCPA/CPRA), consent management platforms, and privacy-compliant marketing measurement strategies. 

What You’ll Bring

• Demonstrated ability to communicate complex analytical findings to marketing stakeholders
• Working knowledge of HTML preferred
• Familiarity with Salesforce data preferred
• Experience in the technology industry preferred
• Knowledge of key requirements for an optimal enterprise data architecture for analytics
• Familiarity with version control (Git) and collaborative development workflows preferred

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