Tech Holding - BI Engineer (Contract)
Requirements
• Employment Type: • 5+ years of experience in Business Intelligence, analytics engineering, or a closely related field. • Strong hands-on expertise with Amazon QuickSight (datasets, SPICE, calculated fields, parameters, RLS, dashboard design). • Proven experience migrating from Power BI to QuickSight (or comparable BI-to-BI migrations). • Strong SQL skills, including complex queries, window functions, and performance tuning. • Solid understanding of dimensional data modeling, semantic layers, and star/snowflake schemas. • Experience defining and standardizing enterprise KPIs, business glossaries, and data dictionaries. • Working knowledge of the AWS analytics stack, including Amazon Redshift, Amazon Athena, and Amazon S3 as reporting sources. • Experience building self-service analytics environments and enabling non-technical business users. • Strong data storytelling and dashboard UX skills; ability to translate ambiguous business needs into clear, actionable visuals. • Familiarity with Git, CI/CD, and version-controlled BI development practices. • Excellent analytical, troubleshooting, stakeholder-management, and communication skills. • Familiarity with Amazon Bedrock, natural language / conversational analytics, or LLM-assisted BI. • Experience with Power BI (DAX, Power Query) for migration analysis. • Knowledge of AWS Lake Formation, AWS Glue Data Catalog, and metadata/lineage tooling. • Exposure to predictive analytics outputs and visualizing model results. • AWS Certification (Data Engineer, Data Analytics Specialty, or Solutions Architect) is a plus.
Responsibilities
• Design and build executive dashboards covering key business, operational, and performance metrics. • Develop longitudinal cohort analysis and self-service analytics capabilities for business stakeholders. • Build and optimize Amazon QuickSight semantic datasets, including SPICE capacity, refresh, and performance tuning. • Migrate prioritized Power BI dashboards and reports to Amazon QuickSight. • Partner with data engineering to define semantic layers, canonical data models, and standardized enterprise KPI definitions across multiple business units. • Contribute to the enterprise business glossary, data dictionary, and KPI catalog, ensuring consistent business definitions across all reporting. • Support natural language and conversational analytics experiences, enabling business users to query metrics in plain language. • Configure knowledge repositories that leverage the business glossary, KPI catalog, and semantic definitions for AI-assisted data exploration. • Conduct dashboard validation, user feedback sessions, and iterative refinement with business stakeholders. • Establish reporting standards, row-level security, and governance for dashboards and datasets. • Participate in UAT, production deployments, knowledge transfer, and post-production support.
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