3. Required Technical Skills
· Tableau dashboard development and support: Strong hands-on experience building, maintaining, debugging and supporting production Tableau dashboards and reports.
· Tableau calculations and interactivity: Proficiency with calculated fields, table calculations, parameters, filters, sets, level-of-detail expressions and dashboard actions.
· Tableau data connectivity and refreshes: Experience working with published data sources, extracts and live connections, and troubleshooting refresh or data-source issues.
· SQL and data validation: Strong SQL capability for joins, aggregations, window functions, source-to-report checks, data-completeness analysis and defect investigation.
· Dashboard performance optimisation: Ability to diagnose slow dashboards and improve workbook design, calculations, data-source usage and refresh efficiency.
· Reconciliation and data quality: Ability to validate totals, identify duplicates or missing records, analyse source-to-dashboard mismatches and protect reporting accuracy.
· Production support and SLA handling: Experience prioritising business-impacting incidents, providing workarounds, performing RCA and delivering permanent fixes within agreed timelines.
· Testing, documentation and communication: Ability to perform regression validation, document changes and clearly communicate incident status, risks, blockers and resolution details.
4. Good-to-Have Skills
· Working knowledge of dbt and upstream analytics data models.
· Working knowledge of PySpark / Apache Spark and distributed data processing.
· Experience with Apache Airflow and CI/CD-based release processes.
· Hive or Hive-compatible data warehousing experience.
· Metabase dashboard and report support experience.
· Tableau Server or Tableau Cloud administration and content-governance exposure.
· Experience with WMS, SCM, inventory, transfer orders, shipment, installation, capitalization or fixed-asset analytics.
5. Functional and Operational Capabilities
· Ability to understand existing dashboard logic and translate business issues into Tableau-, data-source- or upstream-model-level fixes.
· Structured troubleshooting across source data, reporting models, refresh schedules, Tableau data sources, workbooks and dashboards.
· Strong attention to data accuracy, reconciliation integrity, performance and regression prevention.
· Ability to distinguish defects, minor enhancements and out-of-scope redesign requests.
· Strong ownership of production issues and a quality-focused approach to testing and controlled deployment.
· Clear written and verbal communication for incident updates, RCA reviews, blockers, risks and delivery status.