Data Engineer

Ford Motor Company; India; On-site; INR 1,800,000 - 2,400,000; Full time

Job summary

Ford Motor Company is seeking a data-focused software engineer who writes robust Python and collaborates with data scientists, ML engineers, and product managers to train, deploy, and monitor ML models in production.

You will design scalable ETL pipelines on Google Cloud, optimize BigQuery, and implement data quality, CI/CD, and MLOps practices, partnering across teams to deliver reliable data products at scale.

Qualifications

• Degree in Computer Science, Engineering, Mathematics, or related field.
• 4–6 years of professional data or software engineering experience.
• Advanced Python with data processing libraries (PySpark, Pandas, NumPy).
• Experience with API development and production-grade code.
• Hands-on GCP experience (BigQuery, GCS, Cloud Functions/Run).
• Familiarity with ML lifecycle, feature engineering, and deployment.
• Understanding of MLOps, model registry, and continuous training.

Responsibilities

• Design, build, and maintain scalable ETL/ELT data pipelines in Python on GCP.
• Develop feature stores and data feeds for ML training and inference with ML engineers.
• Ingest data from APIs, streaming sources, and on-prem databases into BigQuery and GCS.
• Optimize BigQuery architecture, partitioning, and complex SQL for performance and cost.
• Promote Python best practices: Git, CI/CD, code reviews, testing.
• Implement data quality checks, alerting, and monitoring for AI data reliability.

Skills

Advanced Python

Data processing

APIs

Production-grade code

CI/CD

GCP

ML lifecycle

MLOps

Education

Bachelor’s or Master’s in CS/Engineering/Math

Tools

BigQuery

GCS

Cloud Functions

Cloud Run

Terraform

Docker

Vertex AI

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.