Senior Data Engineer

Experian

13h•Remote

About The Position

We are looking for a Data Engineer with a strong interest in AI, automation, and progress. You will design scalable data platforms that power analytics, AI/ML use cases, and business-critical reporting. You will go beyond traditional ETL—combining modern data engineering practices with AI‑assisted development, automation, and progress. You will design, and maintain scalable, production‑grade data pipelines across cloud data platforms (e.g., AWS Redshift, S3, MWAA/Airflow). Work as part of a team to build and support a data warehouse, implement solutions using Python to process structured and unstructured data. Use SQL and data to understand and troubleshoot existing complex SQL. Partner with Data Science and analytics teams to operationalize data for AI use cases, including feature pipelines, model inputs, and downstream use. Apply AI‑assisted development tools (e.g., code generation, automation, validation) to improve developer productivity and reduce manual effort. Participate in production support and incident resolution, with a focus on root cause analysis and prevention. Contribute to data standards, best practices, documentation, and reusable frameworks that accelerate delivery across teams. Participate in a production on-call rotation.

Requirements

5+ years of experience in Data Engineering development
5 years of experience in Python scripting
3 years of experience with AWS ecosystem (Redshift, EMR, S3, MWAA)
5 years of experience in Agile development method.
Understand complex metrics and translate our requirements to technical requirements
BS degree or higher in computer science or related fields

Responsibilities

Design, and maintain scalable, production‑grade data pipelines across cloud data platforms (e.g., AWS Redshift, S3, MWAA/Airflow).
Work as part of a team to build and support a data warehouse, implement solutions using Python to process structured and unstructured data.
Use SQL and data to understand and troubleshoot existing complex SQL.
Partner with Data Science and analytics teams to operationalize data for AI use cases, including feature pipelines, model inputs, and downstream use.
Apply AI‑assisted development tools (e.g., code generation, automation, validation) to improve developer productivity and reduce manual effort.
Participate in production support and incident resolution, with a focus on root cause analysis and prevention.
Contribute to data standards, best practices, documentation, and reusable frameworks that accelerate delivery across teams.
Participate in a production on-call rotation.