• Advise clients and media buying teams on channel event instrumentation requirements and user attribution, such as Meta’s Conversions API (CAPI), Google Enhanced Conversions, TikTok Events API, and other platform-specific integrations. • Validate and architect web and app analytics event pipelines in tools such as Google Tag Manager and Mobile Measurement Partners such as AppsFlyer, Adjust, or Branch. • Design, execute, and analyze matched-market (geo-lift) incrementality tests using tools such as Meta’s GeoLift library and other ML tooling within Jupyter notebooks. • Contribute to and support Marketing Mix Modeling (MMM) initiatives, including data preparation, model interpretation, and client-facing presentation of results. • Write data ingest pipelines and API integrations using Python running on the Google Cloud Platform (GCP). • Create BI dashboards and views using Looker. • Write data pipelines using SQL in Data Build Tool (dbt) on Google BigQuery. • Investigate data discrepancies and troubleshoot pipeline and reporting issues across web, app, and advertising platforms. • Integrate web, app, eCommerce (e.g., Shopify), and CRM (e.g., Salesforce) data with advertising channel audience and conversion APIs (e.g., Facebook CAPI, Google Enhanced Conversions). • Implement custom BI visualizations using HTML/CSS/JS + Vue. • Implement OAuth endpoints, web callbacks, and other API integrations supporting data and reporting integrations in Python.

• Advise clients and media buying teams on channel event instrumentation requirements and user attribution, such as Meta’s Conversions API (CAPI), Google Enhanced Conversions, TikTok Events API, and other platform-specific integrations. • Validate and architect web and app analytics event pipelines in tools such as Google Tag Manager and Mobile Measurement Partners such as AppsFlyer, Adjust, or Branch. • Design, execute, and analyze matched-market (geo-lift) incrementality tests using tools such as Meta’s GeoLift library and other ML tooling within Jupyter notebooks. • Contribute to and support Marketing Mix Modeling (MMM) initiatives, including data preparation, model interpretation, and client-facing presentation of results. • Write data ingest pipelines and API integrations using Python running on the Google Cloud Platform (GCP). • Create BI dashboards and views using Looker. • Write data pipelines using SQL in Data Build Tool (dbt) on Google BigQuery. • Investigate data discrepancies and troubleshoot pipeline and reporting issues across web, app, and advertising platforms. • Integrate web, app, eCommerce (e.g., Shopify), and CRM (e.g., Salesforce) data with advertising channel audience and conversion APIs (e.g., Facebook CAPI, Google Enhanced Conversions). • Implement custom BI visualizations using HTML/CSS/JS + Vue. • Implement OAuth endpoints, web callbacks, and other API integrations supporting data and reporting integrations in Python.

• Build and iterate on Weaviate Cloud products and product-led-growth (PLG) levers - including onboarding flows, in-product prompts, and expansion paths - with a focus on usability and feature adoption • Identify and resolve friction in the Weaviate Cloud user journey by instrumenting end-to-end flows, tracking key product metrics (activation, engagement, retention), and acting on data insights • Talk to our users directly to build firsthand empathy for the developer experience and surface insights that data alone won’t reveal • Partner cross-functionally with Product, Design, Growth, Web, and Documentation teams to run experiments and create a cohesive user experience across all touchpoints • Contribute to frontend and backend systems that support scalable, high-quality product experiences, including code reviews, automated tests, and production issue resolution • Bring a strong sense of ownership — ship features, measure impact, and iterate quickly.

• Build and maintain responsive UIs using React or Angular, HTML5, and CSS3 • Develop and maintain RESTful APIs and microservices using Node.js, Python, or .NET • Work with relational (MS SQL, PostgreSQL) and NoSQL databases • Participate in cloud deployments on Azure, GCP, or AWS • Actively participate in code reviews, unit testing, and CI/CD deployment pipelines • Use Git for version control and collaborate with QA, product, and design partners within the team delivery model • Use AI-assisted development tools (GitHub Copilot Enterprise, Claude Code) as a default part of the SDLC