Business Intelligence Specialist

Key Responsibilities:

• Design, develop, and maintain enterprise-grade Power BI dashboards, scorecards, and reporting solutions that provide actionable insights into Data Quality and operational performance.
• Build and manage Power BI semantic models, including data modelling, advanced DAX, reusable measures, governed datasets, and reporting standards.
• Optimize and govern Power BI environments, including performance tuning, row-level security, deployment pipelines, workspace management, and Power BI Gateway administration to support secure and automated data refresh processes.
• Design and implement automated dashboard refresh, scheduling, distribution, and monitoring processes to ensure timely and reliable delivery of business-critical insights.
• Define and implement Data Quality KPIs and scorecards across completeness, accuracy, consistency, timeliness, validity, uniqueness, and referential integrity.
• Develop and enhance automated data validation, monitoring, and reporting frameworks using SQL, Python, and cloud-native technologies preferably GCP.
• Perform data profiling, anomaly detection, reconciliation, root-cause analysis, and trend analysis to identify and resolve data quality issues.
• Apply data storytelling techniques to transform complex data quality metrics into clear, compelling narratives and executive-ready insights that drive informed business decisions.
• Partner with Data Engineering teams to understand data models, source-to-target flows, pipeline dependencies, and operational data quality challenges.
• Collaborate with business stakeholders, product owners, and engineering teams to understand requirements and translate them into scalable reporting and monitoring solutions.
• Communicate complex data quality findings, trends, risks, and recommendations clearly to both technical and business audiences while driving continuous improvement initiatives.
• Leverage AI-assisted development, intelligent automation, and modern engineering practices, while mentoring team members and promoting best practices across Power BI, Data Quality, SQL, Python, and data modelling.

Key Requirements:

• 8+ years of experience in Data Quality, Data Engineering, Business Intelligence, Analytics Engineering, or related data platform roles.
• Advanced hands-on Power BI expertise (Level 6+ preferred) with the ability to design, build, optimize, secure, and deploy enterprise reporting solutions.
• Strong experience in Power BI semantic modelling, including star schemas, fact/dimension modelling, governed datasets, and reusable reporting assets.
• Advanced proficiency in DAX, including KPI development, time intelligence, variance analysis, and performance optimization.
• Strong SQL and Python skills for data analysis, automation, root-cause investigation, and data quality validation.
• Strong hands-on experience with Power BI Gateway, automated dashboard refresh scheduling, report distribution, and operational management of enterprise Power BI environments.
• Hands-on experience with data profiling, reconciliation, exception management, automated monitoring, and Data Quality KPI frameworks.
• Experience designing or supporting ETL/ELT pipelines and collaborating with Data Engineering teams on source-to-target data flows and data models.
• Experience with cloud-native data platforms, preferably GCP (BigQuery, Dataflow, Dataproc, Composer/Airflow, Cloud Storage, Pub/Sub), and modern engineering practices including CI/CD and observability.
• Strong understanding of Data Governance, Metadata Management, Data Lineage, Data Observability, and Data Quality best practices.
• Excellent communication, stakeholder management, and mentoring skills, with experience working in Agile environments and globally distributed teams.

Preferred Qualifications:

• Experience establishing Power BI modeling, DAX, dashboard design, governance, and deployment standards across a team or organization.
• Exposure to AI-assisted development tools (Copilot, Gemini, Claude, Copilot Studio) and familiarity with GenAI/Agentic AI concepts.
• Experience establishing Power BI governance, reporting standards, and reusable data quality frameworks across teams.
• Understanding of GenAI, Agentic AI, prompt engineering, retrieval-augmented generation, MCP/A2A concepts, or agent-based orchestration frameworks.
• Experience building reusable data quality rule repositories, metadata-driven validation frameworks, or automated exception management solutions.
• Experience presenting data quality metrics, operational risks, and remediation recommendations to senior stakeholders.