Arizent - Data & AI Engineer
Requirements
• 1+ years of experience in a data engineering, analytics engineering, and/or AI/ML engineering role. • Proficiency in SQL and Python. • Hands-on experience building agentic solutions, including prompt engineering, tool/function calling, and deploying agents in production using platforms like Google Gemini, Claude, or comparable LLM ecosystems. • Experience designing and building ETL pipelines and integrating multiple data sources. • Strong working knowledge of the Google Cloud ecosystem, especially BigQuery. • Dashboarding experience with Data Studio or a comparable BI tool. • Demonstrated ability to collaborate cross-functionally and translate business needs into technical solutions. • Strong communication skills, with the ability to explain technical concepts to non-technical stakeholders. • Experience with Agent Development Kit (ADK) or other multi-agent orchestration frameworks. • Familiarity with Matillion or comparable ETL/ELT tooling. • Experience with Omeda, Salesforce, Piano, or similar marketing/audience platforms. • Prior experience in media, publishing, or B2B information services. • Experience designing data models and warehouse schemas from scratch. • Tools and Systems Used • BigQuery, Data Studio, Google Gemini, Claude, Python, SQL, Matillion, Omeda, Salesforce, Google Analytics, Piano, Google Workspace. • Cross-Functional Collaboration • Works closely with members of the Sales, Marketing, Demand Gen, Editorial, Finance, Product, OLT teams. • Time Allocation • 80% - AI and Agentic Solutions • 10% - Database Management • 10% - Data Troubleshooting
Responsibilities
• AI & Agentic Solutions (80%) • Build and deploy agentic solutions on Google Gemini and Claude, ranging from no-code/low-code workflows to multi-agent systems built with Agent Development Kit (ADK). • Partner with teams across Arizent to identify use cases where agents can address real business needs. In collaboration with the Data & AI team, you will develop the full lifecycle from solution mapping to building, testing, agent training, and end-user enablement. • Maintain and continuously test deployed agents to ensure reliability, accuracy, and performance over time. • Collaborate with internal stakeholders and vendors to forecast and measure the impact of agentic solutions, including cost savings, time savings, and quality improvements. • Data Warehouse & Pipeline Management (10%) • Support the Senior Manager of Data & AI in maintaining our BigQuery data warehouse following established workflows and governance practices. • Design, build, and manage ETL pipelines integrating Omeda, Salesforce, Google Analytics, and third-party services (Reach Marketing, Discovery, Bombora, and others). • Set up and manage scheduled queries to keep dashboards and downstream systems current. • Support audience enrichment, lead and contact enrichment, and internal/external reporting workflows. • Data Quality, Troubleshooting & Reporting (10%) • Troubleshoot site traffic tracking and data collection issues across Google Analytics, Omeda, Piano, and Salesforce to preserve data integrity. • Build alerts and monitoring dashboards that surface tracking issues before they affect reporting. • Working closely with Data & AI team leadership to develop and maintain internal and client-facing dashboards in Data Studio that give stakeholders clear views of their KPIs. • Key Performance Indicators (KPIs) or Success Metrics • Data quality: Reporting across the business is consistently accurate and trusted, with measurable reductions in tracking errors and data discrepancies. • Self-service access: Dashboards and recurring reports give internal teams timely, reliable access to the KPIs they need without bottlenecks. • Agentic impact: Deployed agents demonstrate measurable cost or time savings, with documented ROI and adoption across teams.
Benefits
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