Senior Data Engineer: Python & GCP ML Pipelines

Ford; Hinoba-an; On-site; PHP 1,200,000 - 1,800,000; Full time

Job summary

Ford is seeking a skilled Data Engineer with advanced Python programming to design scalable data pipelines and apply robust software engineering practices to ML-ready data problems. You will collaborate with Data Scientists, ML Engineers, and Product Managers to train, deploy, and monitor models in production.

Responsibilities include building ETL/ELT pipelines in Python on GCP (Cloud Run, Cloud Functions), optimizing BigQuery architecture, ingesting data from APIs, streams, and on-prem sources

Qualifications

• Bachelor’s or Masters in CS/Engineering/Math or equivalent practical experience.
• 4–6 years in Data or Software Engineering.
• Advanced Python with data processing libraries.
• Experience with GCP and BigQuery architecture.
• Familiarity with API development and production-grade code.
• Knowledge of MLOps and data governance is a plus.

Responsibilities

• Design and maintain scalable ETL/ELT pipelines using Python and GCP.
• Ingest data from APIs, streaming platforms, and on-prem sources into BigQuery and GCS.
• Optimize BigQuery performance through partitioning and SQL tuning.
• Collaborate with Data Scientists, ML Engineers, and PMs to operationalize models.
• Implement code quality, CI/CD, testing, and git workflows.

Skills

Advanced Python

API development

CI/CD practices

Git

Education

Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or related field

Tools

PySpark

Pandas

NumPy

BigQuery

GCS

Cloud Functions

Cloud Run

Terraform

Docker

Job description

Ford is seeking a skilled Data Engineer with advanced Python programming to design scalable data pipelines and apply robust software engineering practices to ML-ready data problems. You will collaborate with Data Scientists, ML Engineers, and Product Managers to train, deploy, and monitor models in production.

Responsibilities include building ETL/ELT pipelines in Python on GCP (Cloud Run, Cloud Functions), optimizing BigQuery architecture, ingesting data from APIs, streams, and on-prem sources