Gathern | جاذر إن

Business Intelligence Developer

• Ensure compliance with relevant regulations while promoting organizational transparency.
• Develop and maintain business dashboards and reports using Power BI or similar tools.
• Analyze trends, patterns, and relationships across business metrics.
• Perform root-cause analysis on KPI movements and anomalies.
• Build and maintain basic forecasting and projection models for key business metrics.
• Support scenario analysis and forward-looking insights (best / expected / worst cases)
• Use Python-based analysis and plotting when deeper exploration or custom visualization is required.
• Translate business questions into analytical frameworks and metrics.
• Partner with stakeholders (Product, Finance, Operations, Engineering) to provide decision-ready insights.
• Contribute to KPI definitions, metric documentation, and governance.
• Present insights clearly to non-technical audiences, including leadership.
• Strong proficiency in SQL for analytical querying and data exploration.
• Hands-on experience with BI tools such as PowerBI, including (Building dashboards and reports - Working with semantic models and measures - Applying filters, drill-downs, and row-level security concepts).
• Solid understanding of analytical concepts, including (Trend analysis - Forecasting and projections - Correlation vs causation - Variance and comparative analysis).
• Experience working with data warehouses (BigQuery preferred).
• Working knowledge of Python for analysis, including (Data exploration and transformation (e.g. pandas) - Plotting and visualization (e.g. matplotlib, seaborn, or equivalent) - Using Python notebooks or scripts to support analysis).
• Familiarity with data validation techniques to ensure analysis accuracy.

Business Intelligence Analyst

Data Engineer

• Design, build, and maintain scalable data pipelines feeding the central data warehouse (BigQuery).
• Ensure data accuracy, freshness, reliability, and consistency across all BI and analytics datasets.
• Own data modeling, schema design, and documentation standards.
• Support efficient onboarding of new data sources with minimal rework.
• Optimize data access patterns and performance for BI tools (e.g., Power BI).
• Implement and maintain data quality checks and monitoring mechanisms.
• Define and enforce core data quality dimensions (accuracy, completeness, consistency, timeliness, validity).
• Build automated validation checks (freshness SLAs, null thresholds, schema drift detection, referential integrity).
• Monitor data quality metrics and proactively alert on issues before they impact decision-making.
• Troubleshoot, debug, and resolve data pipeline and data quality incidents.
• Partner closely with BI, Analytics, Product, and Engineering teams to support reporting and insight needs.
• Enable self-service analytics through well-designed, documented, and trusted datasets.
• Clearly document technical decisions, data models, and pipeline logic.
• Support data access controls, permissions, and security best practices in cloud environments.