Data & BI
Hire QuickSight Developers
Analytics engineers who deliver reporting inside an AWS estate without standing up a second data platform.
QuickSight earns its place when the data already lives in AWS and the alternative is licensing and operating another platform beside it. Serverless capacity, per-reader pricing and native integration with Athena, Redshift, S3 and IAM make it a pragmatic choice, particularly for embedding analytics into a product a customer already logs into.
When hiring, ask about embedding and about cost, because those are the two places this tool is usually chosen and usually misused. How did they isolate tenants, what did row-level security cost them in complexity, and how did they keep SPICE refreshes and Athena scans within budget. Ask also what they would not attempt in QuickSight, since honest limits are a good sign of real use.
What these engineers do
- SPICE dataset design, refresh scheduling and cost-aware capacity planning
- Athena, Redshift and RDS sources with the heavy lifting tuned at the source
- Embedded analytics through the QuickSight SDK with per-tenant row-level security
- Calculated fields, parameters and controls that make self-service genuinely usable
- Assets defined in CloudFormation or Terraform so dashboards are versioned, not clicked
Delivered AI-first
AI assistance is used to generate the Athena and Redshift SQL behind datasets, to draft calculated fields and parameter logic, and to write the infrastructure as code definitions for datasets, analyses and permissions that QuickSight otherwise encourages you to create by hand. Query cost is reviewed before anything is scheduled, since an unoptimised Athena scan repeated hourly is a bill rather than a bug report. Review discipline is unchanged. The measurable effect is throughput per engineer, not fewer reviews.