Data Analyst — Feature Engineering & BI Integration

Energy Innovation Corp; Charleston (SC); On-site; USD 72,000 - 103,000; Full time

Benefits offered by this job

Real mentorship

Lasting benefits

Flexible work arrangements

Job summary

Retail Solutions Group in Charleston, SC is seeking a Data Analyst whose decisions on feature engineering age gracefully. The role offers a salary range of $72,000–$103,000 for a temporary 3-year contract, with mentorship and lasting benefits as well as real flexibility.

You will configure infrastructure as code, help prevent SageMaker regressions in staging, and drive a feature rewrite that reduces technical debt.

Qualifications

• Teammate's instinct to unblock others before yourself.
• Proven leadership experience guiding mid-level initiatives.
• Bias toward asking the dumb question before the expensive mistake.
• Customer-focused outlook with strong interpersonal skills.
• Adaptability and resilience when facing shifting requirements.

Responsibilities

• Configure and manage infrastructure as code across staging and production.
• Catch the SageMaker regression in staging before it reaches Charleston customers.
• Build the Delegation feature that wins back SC accounts.
• Rewrite feature engineering solutions to reduce years of technical debt.
• Wire Delegation APIs to Power BI consumers so data lands with Charleston teams.
• Keep Power BI service humming through Charleston's holiday traffic surge.
• Support migration of on-premise services to cloud-native architecture.
• Harden Python auth so the SC audit comes back clean.

Skills

Team player mindset

Leadership experience

Curiosity

Customer-focused

Adaptability

Tools

Power BI

Python

SageMaker

Job description

Retail Solutions Group in Charleston, SC is seeking a Data Analyst whose decisions on feature engineering age gracefully. The role offers a salary range of $72,000–$103,000 for a temporary 3-year contract, with mentorship and lasting benefits as well as real flexibility.

You will configure infrastructure as code, help prevent SageMaker regressions in staging, and drive a feature rewrite that reduces technical debt.