Responsibilities
- Build and manage Power BI dashboards, reports, scorecards, KPIs, semantic models, datasets, and standardized metrics to enable enterprise-wide reporting and informed decision-making.
- Convert business needs into robust, intuitive analytics platforms emphasizing visual clarity, efficiency, ease of use, and widespread user adoption.
- Produce analytics outputs suitable for executive review that effectively convey insights to both technical and non-technical stakeholders and lead to measurable business impact.
- Create and sustain dimensional data models such as star and snowflake schemas in alignment with enterprise data architecture and modeling guidelines.
- Construct and maintain scalable ETL and ELT workflows, data pipelines, and system integrations for analytical processing.
- Collaborate with data engineering teams to enhance data accessibility, reliability, speed, and readiness for reporting.
- Maintain high standards for data accuracy, completeness, consistency, integrity, and compliance with governance, security, privacy, and quality policies.
- Conduct testing, validation, reconciliation, root cause analysis, and troubleshooting across unit, integration, regression, and user acceptance phases.
- Document data definitions, business rules, lineage, calculation logic, and test results to ensure transparency and trust in analytics outputs.
- Work closely with business stakeholders to understand operations, reporting demands, strategic goals, and areas where analytics or AI can drive improvement.
- Lead requirements collection, translate business needs into technical specs, and manage solution reviews, demonstrations, user acceptance testing, and training sessions.
- Act as a strategic advisor by offering data-backed recommendations that enhance performance and operational effectiveness.
- Find ways to automate manual tasks, increase efficiency, and encourage the use of self-service analytics tools.
- Assess and deploy new analytics technologies, AI features, standards, reusable components, and accelerators to improve delivery speed and quality.
- Promote a culture of innovation, ongoing learning, teamwork, and decisions grounded in data.
- Design analytics workflows powered by AI, using prompt engineering for BI, reporting, data exploration, automation, and decision support.
- Utilize tools like Snowflake Cortex Analyst, Power BI Copilot, Microsoft Copilot, and GitHub Copilot to speed up development and improve results.
- Develop and manage semantic models, business metrics, data abstractions, and trusted semantic layers ready for AI and natural language querying.
- Build and support AI agents, agentic analytics systems, API/MCP integrations, and LLM-driven workflows that securely connect to governed data sources.
- Evaluate, verify, and monitor insights generated by AI for correctness, dependability, compliance, business value, and adherence to responsible AI practices.
