Sr. Analytics Engineer: Data Pipelines & AI

AES Corporation; San Francisco (CA); On-site; USD 113,000 - 142,000; Full time

Benefits offered by this job

Annual bonus

Medical, dental, vision insurance

Job summary

AES Corporation is seeking a Senior Analytics Engineer to support the O&M Engineering team with scalable data architectures and analytics for the growing AES Clean Energy (CE) portfolio. You will drive data availability, quality, and insights across stakeholders in the Data Analytics group.

The role emphasizes building data warehouses, dashboards, data pipelines, and AI/ML initiatives to optimize solar, wind, and energy storage assets, requiring expertise in SQL, Python/R, and Power BI among

Qualifications

• Undergraduate degree in Engineering, CS, or equivalent and 5+ years in analytics-focused/engineering roles.

Responsibilities

• Designing, building and maintaining data warehouses, data cleansing processes, analytics dashboards, pipelines, and automated reporting solutions.
• Maintaining data quality, reliability and availability across systems and workflows.
• Owning and administering core data engineering and analytics platforms used by the Data Analytics team.
• Collaborating with Analytics Engineering, O&M, and Digital teams to deliver scalable data solutions.
• Working on AI/ML projects to improve performance of AES Clean Energy assets.

Skills

5+ years analytics

R/Python/SAS

SQL

Power BI/Tableau/Qlik Sense

DAS/SCADA data

GCP/BigQuery

ETL/ELT pipelines

Data modeling

Excel/Office

Strong communication

Education

Engineering degree

Tools

Power BI

Tableau

Qlik Sense

DBT

Airflow

Prefect

SQL

Python

Job description

AES Corporation is seeking a Senior Analytics Engineer to support the O&M Engineering team with scalable data architectures and analytics for the growing AES Clean Energy (CE) portfolio. You will drive data availability, quality, and insights across stakeholders in the Data Analytics group.

The role emphasizes building data warehouses, dashboards, data pipelines, and AI/ML initiatives to optimize solar, wind, and energy storage assets, requiring expertise in SQL, Python/R, and Power BI among