Senior Business Intelligence Engineer

GCS Recruitment; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

GCS Recruitment Specialists seeks a Senior BI Engineer focused on Quicksight to own the manufacturing reporting layer, validate business metrics, and rebuild dashboards for scalability. You will work closely with operations, software engineering, and data engineering teams to ensure accurate, trusted, and automated operational reporting.

The role emphasizes data modeling, dashboard design, and leveraging AWS data platforms like Athena, Redshift, S3, Glue, and PySpark to deliver self-service

Qualifications

• 10+ years of experience in BI, Analytics Engineering, or Data Engineering.
• Experience owning the reporting layer end-to-end, from source data through dashboards.
• Strong SQL expertise with complex joins, window functions, and performance tuning.
• Hands-on experience with Amazon QuickSight (SPICE, calculated fields, row-level security).
• Strong AWS experience with Athena, Redshift, S3, Glue, and PySpark.

Responsibilities

• Own the data modeling and reporting layer for manufacturing dashboards.
• Design and optimize data models to support intuitive dashboards for business users.
• Assess existing dashboards and datasets, improve data quality and reporting workflows.
• Validate KPIs and metrics with operations stakeholders.
• Rebuild and optimize the reporting layer from source tables to dashboards.
• Develop automated WBR/MBR dashboards using Amazon QuickSight.
• Collaborate with operations, software engineering, and data engineering teams.

Skills

Data modeling

SQL

Amazon QuickSight

AWS data platforms

Python / PySpark

BI / Analytics Engineering

End-to-end reporting ownership

Tools

Athena

Redshift

S3

Glue

Job description

Hiring Alert

Senior BI Engineer - (Quicksight, Data Modeling and AWS Data Stack)

We are looking for a Senior BI Engineer - Quicksight with strong data modeling experience, and owning and rebuilding the manufacturing reporting layer, ensuring accurate, trusted, and automated operational reporting. The ideal candidate will work closely with operations, software engineering, and data engineering teams to validate business metrics, redesign datasets, and deliver scalable QuickSight dashboards.

Key Responsibilities

• Strong data modeling experience, including designing fact and dimension schemas from business requirements and creating intuitive data models for business users.
• Designing the data model. Identify a sensible, simple model (fact and dimension tables) that would yield those dashboards
• Assess existing QuickSight dashboards, Athena/Redshift datasets, and reporting workflows.
• Analyze and resolve data quality and reporting discrepancies.
• Validate KPIs and business metrics with operations stakeholders.
• Rebuild and optimize the reporting layer from source tables through dashboards.
• Develop and automate WBR/MBR dashboards using Amazon QuickSight.
• Work with AWS data platforms including Athena, Redshift, S3, Glue, and PySpark.
• Implement reporting logic for calendars, shifts, utilization, throughput, cycle times, on-time delivery, yield, and backlog metrics.
• Create self-service datasets, templates, and documentation for business users.
• Support future analytics, data, and automation initiatives.

Required Skills

• 10 years of experience in BI, Analytics Engineering, or Data Engineering.
• Experience owning the reporting layer end-to-end, from source data through dashboards.
• Strong SQL expertise, including complex joins, window functions, and performance tuning.
• Hands-on experience with Amazon QuickSight (SPICE, calculated fields, row-level security, dashboards).
• Strong AWS experience with Athena, Redshift, S3, Glue, and PySpark.
• Python/PySpark experience for data validation, profiling, and transformation.
• Experience working with operational, manufacturing, supply chain, logistics, or production data.

Preferred Skills

• Manufacturing, MES, ERP, or shop-floor analytics experience.
• Experience supporting WBR/MBR reporting and KPI frameworks.
• Knowledge of operational metrics such as utilization, throughput, downtime, cycle time, yield, and on-time delivery.