Hybrid Analytics Engineer: Data Pipelines & AI

Oliver Wyman; Mexico; On-site; PHP 7,524,000 - 10,031,000; Full time

Job summary

Oliver Wyman is seeking an experienced Advanced Analytics Engineer to design and build scalable data solutions that support advanced analytics, machine learning, and AI initiatives. The role focuses on developing data pipelines and workflows for structured and unstructured data using Databricks, Python, and PySpark.

You will collaborate with data scientists, AI engineers, and business stakeholders to implement data quality, governance, and observability, and contribute to CI/CD DataOps practices.

Qualifications

• 2+ years of experience in Data Engineering, Analytics Engineering, or related roles.

Responsibilities

• Design, develop, and optimize scalable data pipelines using PySpark and Databricks
• Build workflows to ingest, process, and transform structured and unstructured data
• Develop data models and reusable datasets for analytics and AI use cases
• Integrate external systems and enterprise platforms through APIs and modern data interfaces
• Collaborate with AI engineers and platform teams to support MCP integrations and AI-driven workflows
• Collaborate with data scientists, AI engineers, and business stakeholders to support advanced analytics initiatives
• Implement data quality, governance, monitoring, and observability best practices
• Optimize performance and scalability of distributed data processing environments
• Support batch and real-time data processing architectures
• Contribute to CI/CD pipelines and DataOps best practices
• Document technical solutions, workflows, and operational procedures

Skills

Python

PySpark

Databricks

SQL

API integration

Cloud platforms

Tools

Databricks

CI/CD pipelines

Job description

Oliver Wyman is seeking an experienced Advanced Analytics Engineer to design and build scalable data solutions that support advanced analytics, machine learning, and AI initiatives. The role focuses on developing data pipelines and workflows for structured and unstructured data using Databricks, Python, and PySpark.

You will collaborate with data scientists, AI engineers, and business stakeholders to implement data quality, governance, and observability, and contribute to CI/CD DataOps practices.