Senior ML Engineer: Cloud Data Pipelines & MLOps

Tiger Analytics; United States; On-site; USD 140,000 - 210,000; Full time

Job summary

Tiger Analytics is seeking a seasoned software engineer/data engineer to join our AI and analytics practice in the United States. You will design and implement scalable data pipelines using Python and Spark on Azure/Databricks, optimize workflows, and contribute to MLOps; collaborate with DS, DE, Cloud Ops, and product teams to deliver production-grade data solutions.

Ideal candidates bring 5+ years of software development experience, a strong command of cloud data platforms, and a pragmatic

Qualifications

• Proficiency in Python and applying software engineering principles (OOP, FP, design patterns).
• Deep knowledge of cloud-based data platforms (Azure, Databricks) incl. cluster config and Spark optimization.
• Strong experience with distributed data processing (Spark, Delta tables, cloud storage) and building scalable data pipelines.
• Exposure to DevOps and engineering hygiene: Docker, infrastructure-as-code, CI/CD, automated testing for workflows.
• Proven ability to work in cross-functional teams (DS, DE, Cloud Ops, Product) and translate ambiguous requirements into robust solutions.

Skills

Python

Spark

Cloud platforms

Data pipelines

CI/CD

Docker

OOP & design patterns

Observability

Cross-functional teamwork

Tools

MLflow

AzureML

Databricks Model Serving

Databricks Platform

Terraform

Job description

Tiger Analytics is seeking a seasoned software engineer/data engineer to join our AI and analytics practice in the United States. You will design and implement scalable data pipelines using Python and Spark on Azure/Databricks, optimize workflows, and contribute to MLOps; collaborate with DS, DE, Cloud Ops, and product teams to deliver production-grade data solutions.

Ideal candidates bring 5+ years of software development experience, a strong command of cloud data platforms, and a pragmatic