Tableau Developer

Role: Tableau Developer

Location: SFO, CA / Dallas, TX (Hybrid)

Duration: 12 Months

Job Description:

The project focuses on creating dashboards, reports, and monitoring solutions to help teams track the success of a new business initiative, improve data visibility, measure user adoption, and demonstrate business value.

Key Responsibilities

1. Dashboard Development & Reporting

• Design and build interactive dashboards for different business users.
• Create dashboards that track:
• Data quality and system health
• User adoption and engagement
• Business outcomes and impact
• Enable users to analyze trends, compare performance across groups, and drill down into detailed information.

2. Metrics & KPI Management

• Define, develop, and validate key business metrics and KPIs.
• Maintain clear documentation for all metrics and reporting logic.
• Ensure consistent measurement across teams.

3. Data Integration & Modeling

• Connect and consolidate data from multiple sources.
• Design data models and transformations required for reporting.
• Ensure data accuracy, consistency, and governance compliance.

4. User Experience & Stakeholder Support

• Develop dashboards tailored for different audiences, including leadership and operational teams.
• Ensure reports are easy to understand and drive actionable decisions.
• Gather feedback and continuously improve reporting solutions.

5. Ongoing Support & Enhancements

• Maintain and enhance dashboards and reports.
• Fix data and reporting issues as they arise.
• Update metrics and reporting requirements as business needs evolve.

Expected Business Outcomes

• Real-time visibility into program performance and adoption.
• Early identification of risks, data issues, and operational gaps.
• Improved decision-making through data-driven insights.
• Clear measurement of business value and return on investment (ROI).

AI-Driven Data Operations (Agentic Data Ops)

The initiative also includes modernizing Data Operations processes by introducing AI-powered automation to reduce manual effort and improve efficiency.

Key Responsibilities

• Assess current data operations processes and identify automation opportunities.
• Design AI-driven workflows for:
• Data quality monitoring
• Anomaly detection
• Issue prioritization and triage
• Automated remediation recommendations
• Build and prototype intelligent automation solutions.
• Define operating models, governance processes, and best practices.
• Provide training, documentation, and knowledge transfer to internal teams.

Expected Outcomes

• Proactive identification and resolution of data issues.
• Reduced manual operational effort.
• Faster response to data quality problems.
• Scalable and intelligent data governance processes.
• Improved overall data reliability and business trust in data.