Analytics Engineer: Data Platform & Warehousing

Indigo Beam; Houston (TX); On-site; USD 90,000 - 130,000; Full time

Job summary

Indigo Beam Consulting is seeking an Analytics Engineer to design, build, and maintain our enterprise data platform in Houston. You will work across ETL/ELT pipelines, data warehouses, semantic models, and analytics to deliver reliable, scalable data for business users and analytics teams.

The ideal candidate will pair strong SQL development with data quality focus, optimize performance, and collaborate with stakeholders.

Qualifications

• Bachelor’s degree in CS/IS/Analytics or equivalent experience.
• 3+ years in analytics engineering, data warehousing, or BI.
• Strong SQL skills and relational database experience.
• Experience with ETL/ELT pipelines and data integration.
• Experience with Azure Data Factory or similar.
• Knowledge of data modeling and analytical data structures.
• Ability to document technical solutions clearly.

Responsibilities

• Design, develop, and maintain enterprise data warehouse structures.
• Develop efficient SQL queries, stored procedures, transformations.
• Build and maintain ETL/ELT processes.
• Develop and support data pipelines using Azure Data Factory.
• Monitor pipelines, troubleshoot failures.
• Support ongoing evolution of the data platform.
• Create technical documentation and standards.

Skills

SQL

Relational DBs

Data warehousing

ETL/ELT pipelines

Azure Data Factory

Data modeling

Semantic modeling

Documentation

Problem-solving

Education

Bachelor’s degree in Computer Science, Information Systems, Data Analytics, Engineering, or related field

Tools

Power BI

Git

Azure SQL

SQL Server

Job description

Indigo Beam Consulting is seeking an Analytics Engineer to design, build, and maintain our enterprise data platform in Houston. You will work across ETL/ELT pipelines, data warehouses, semantic models, and analytics to deliver reliable, scalable data for business users and analytics teams.

The ideal candidate will pair strong SQL development with data quality focus, optimize performance, and collaborate with stakeholders.