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Mid-level, widely advertised analytics role with common BI/AWS skills and metro hiring drives high competition.
Core analytics engineering skills transfer across industries, but BI semantic modeling causes moderate sensitivity.
Explicit 5–8 years requirement plus mandatory SQL, Redshift and BI expertise enforces high strictness.
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Design and own enterprise-grade data models and semantic layer architecture with focus on KPI standardization, consistency, scalability, and reusability.
Build, optimize, and support complex BI reports, dashboards, and analytical datasets using AWS QuickSight, Tableau, Power BI, and work closely with BI developers and data engineers.
Lead performance optimization including tuning SQL transformations and minimizing dashboard latency, while mentoring junior engineers and enforcing data governance including RLS/CLS models.
5 to 8 years overall analytics experience with hands-on data modeling and BI solution delivery.
Bachelor’s degree in Computer Science, Engineering, or related field.
Expertise in SQL on Redshift or similar MPP systems, strong AWS data ecosystem knowledge, and experience with BI visualization tools like QuickSight, Tableau, or Power BI.
Experience with data governance, security models (RLS/CLS), and large-scale data handling; preferred AWS certifications (not mandatory).
Experienced Analytics Engineer who effectively bridges data engineering and business analytics to deliver scalable, governed data solutions in large, distributed environments.
Strong technical orientation with deep expertise in SQL MPP systems and BI tools, capable of optimizing complex analytics workloads and ensuring data quality.
Proven leadership in mentoring and collaborating across distributed teams, translating business requirements into robust data models and performant analytics platforms.