Data Engineer - EDP & Data Products

Role Overview

We are seeking a mid-senior Data Engineer (Contractor) to support development within our Enterprise Data Platform (EDP). This role focuses on building reliable, scalable data pipelines and curated datasets while improving data reliability, enabling self-service analytics, and reducing manual engineering effort through automation.

The ideal candidate is a hands-on backend engineer who can independently deliver data engineering work and contribute meaningfully to platform maturity and team efficiency.

Role Summary & Key Responsibilities

Design, build, and maintain production-grade data pipelines and curated datasets using Azure Databricks and Azure-native services to support analytics and Power BI. Develop efficient, maintainable SQL for data transformations, validation, and performance optimization. Collaborate with data engineers, analysts, and BI developers to translate business requirements into scalable, governed data models. Ensure data reliability through quality checks, schema change handling, and proactive monitoring to minimize pipeline failures and downstream risk. Leverage automation and AI-assisted tools to improve development speed, reduce manual effort, and standardize engineering workflows. Enable self-service analytics by delivering well-documented, reusable, analytics-ready datasets. Own end-to-end delivery of assigned workstreams, contributing actively to Agile ceremonies, documentation, and engineering best practices.

Required Experience & Skills

4-8 years of experience in data engineering or backend data development. Strong hands-on experience with Azure Databricks (or equivalent platforms) and advanced SQL. Proven experience building data pipelines and models for analytics and BI consumption. Solid understanding of modern data architectures (lakehouse, curated layers, dimensional modeling). Ability to work independently in complex enterprise environments with multiple dependencies.

Preferred (Senior-Level Differentiators)

Experience with data observability frameworks, CI/CD, or DevOps for data pipelines. Demonstrated impact through reusable tooling, templates, or engineering enablement. Experience mentoring junior engineers or providing technical leadership. Background working in regulated, global, or highly matrixed organizations.