Hybrid Data Engineering Manager — AI-Driven Platform

GM Financial; Arlington (TX); Hybrid; USD 120,000 - 170,000; Full time

Benefits offered by this job

401K matching

Tuition assistance

Training

GM employee auto discount

Nine company holidays

Job summary

GM Financial Technology is expanding its data engineering team to build scalable analytics and decision support platforms. You will work on large data sets, ingestions and transformations in batch and streaming contexts, enabling ML training and search‑based analytics.

The role collaborates with data scientists, ETL developers and IT partners to define architecture and standards, while driving automation and deployment best practices.

Qualifications

• 5‑7 years software engineering experience including Java, Scala, Python.
• 5‑7 years processing large data sets with Kafka, Hadoop, Spark or similar systems.
• 2‑4 years scripting with Bash, Perl, Ruby.

Responsibilities

• Develop, test, deploy, monitor, and document data engineering processing.
• Architect end‑to‑end data engineering solutions including batch and streaming transforms.
• Collaborate with data scientists, data architects, ETL developers and IT partners.
• Define data engineering architecture (hardware/software) to meet business requirements.
• Coach ETL developers and support incident management for SLAs.
• Contribute to distributed systems architecture for scalability and reliability.
• Identify opportunities to improve resource utilization and automation.

Skills

Java

Scala

Python

Big data

Hadoop

Spark

Kafka

SQL

Linux

Cloud

Data engineering

ETL

Kubernetes

Beam/Nifi

Education

Bachelor’s Degree in related field or equivalent work or military experience

High School Diploma or equivalent

Tools

Informatica

DataStage

Ab Initio

Cognos

BusinessObjects

Oracle Business Intelligence

Job description

GM Financial Technology is expanding its data engineering team to build scalable analytics and decision support platforms. You will work on large data sets, ingestions and transformations in batch and streaming contexts, enabling ML training and search‑based analytics.

The role collaborates with data scientists, ETL developers and IT partners to define architecture and standards, while driving automation and deployment best practices.