Data Engineer

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

We are looking for someone with advanced Python programming skills who applies robust software engineering principles to data problems. You will collaborate closely with Data Scientists, ML Engineers, and Product Managers to build the scalable, automated pipelines required to train, deploy, and monitor machine learning models in production.

Key Responsibilities

• GCP Pipeline Development:Design, build, and maintain highly scalable ETL/ELT data pipelines using Python and GCP-native data processing tools (e.g., Cloud Run, Cloud Functions).
• AI/ML Infrastructure Support:Engineer feature stores, robust data feeds specifically optimized for machine learning training and inference. Work closely with ML Engineers to operationalize models using Vertex AI.
• Data Integration & Ingestion:Write clean, modular Python code to ingest data from diverse sources (APIs, streaming platforms, on-prem databases) into BigQuery and Google Cloud Storage (GCS).
• System Optimization:Optimize BigQuery architecture, partition/cluster tables, and tune complex SQL queries to ensure performance and cost-efficiency at a massive scale.
• Software Engineering Best Practices:Champion best practices in Python development, including version control (Git), CI/CD pipelines (Cloud Build / GitHub Actions), code reviews, and comprehensive unit/integration testing.
• Data Quality & Governance:Implement robust data quality checks, alerting, and monitoring to ensure the data feeding our AI models is accurate and reliable.

Required Qualifications

• Degree:Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related technical field (or equivalent practical experience).
• Experience:4 to 6 years of professional experience in Data Engineering, Software Engineering, or a closely related field.
• Advanced Python:Deep expertise in Python programming. You should be highly comfortable with:
  • Data processing and ML-adjacent libraries (e.g., PySpark, Pandas, NumPy).
  • API development
  • Writing efficient and production-grade code.
• GCP Mastery:Proven, hands-on experience designing and operating data architectures on Google Cloud Platform. Must have strong experience with:
  • BigQuery(advanced SQL, architecture, and optimization).
  • Google Cloud Storage (GCS).
  • Compute/Serverless (Cloud Functions, Cloud Run).
• AI/ML Acumen:Experience working alongside Data Science teams. A strong understanding of the ML lifecycle, feature engineering, and the data requirements for model training and deployment.

• MLOps:Understanding of MLOps principles, model registry, and continuous training pipelines.

Preferred Qualifications

• Vertex AI:Direct experience interacting with or deploying pipelines using Google Cloud's Vertex AI platform.
• Streaming Technologies:Familiarity with real-time data processing using Google Cloud Pub/Sub and streaming Dataflow jobs.
• Infrastructure as Code:Experience managing GCP resources using Terraform.
• Containerization:Proficiency with Docker.