Senior BI Engineer - Quicksight

GCS Recruitment

United States

Remote

USD 140,000 - 190,000

Full time

2 days ago
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Job summary

GCS Recruitment Specialists is seeking a Senior BI Engineer to own and rebuild our manufacturing reporting layer. You will work with operations, software, and data engineering teams to validate metrics, redesign datasets, and deliver scalable QuickSight dashboards.

The role emphasizes data modeling, SQL proficiency, and deep AWS expertise across Athena, Redshift, S3, Glue, and PySpark, with Python-based validation and production analytics experience.

Qualifications

  • 10+ years in BI, analytics engineering, or data engineering.
  • Own end-to-end reporting from source data to dashboards.
  • Strong SQL with complex joins and window functions.
  • Hands-on with Amazon QuickSight (SPICE, calculated fields, RLS).
  • Strong AWS experience: Athena, Redshift, S3, Glue, PySpark.
  • Python/PySpark for validation, profiling, and transformation.
  • Experience with manufacturing, supply chain, or production data.

Responsibilities

  • Own the reporting layer end-to-end from source to dashboards.
  • Validate KPIs and business metrics with stakeholders.
  • Rebuild and optimize reporting logic across data sources.
  • Develop and automate WBR/MBR dashboards in QuickSight.
  • Design data models (facts and dimensions) for dashboards.
  • Review existing datasets and reporting workflows.
  • Create self-service datasets, templates, and docs for users.
  • Support future analytics and automation initiatives.

Skills

BI/Data Eng
SQL
AWS
Python
ETL/Data Modeling

Tools

QuickSight
Athena
Redshift
S3
Glue
PySpark

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.
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