Data Engineer

The Role

The Data Engineer plays a foundational role in building the data infrastructure that powers Continental's analytics, reporting, and AI initiatives. This person works at the intersection of Continental's business systems and its growing data platform, designing and maintaining pipelines, models, and processes that bring together data from across the organization into a reliable, unified foundation.

Responsibilities

• Design, build, and maintain scalable cloud-native data pipelines—including ETL/ELT, event-driven, and serverless patterns using Azure Functions and related services—that ingest, transform, and load data from Continental’s diverse source systems into a centralized lake or data warehouse.
• Develop and maintain data models within Microsoft Fabric and Azure that support analytics, business intelligence, and emerging AI use cases, ensuring data is structured for both performance and usability.
• Integrate data from disparate systems across Continental’s business units into a unified enterprise data model, resolving inconsistencies and establishing a single source of truth for key business metrics.
• Implement and enforce data quality, validation, and monitoring practices that ensure downstream consumers—analysts, dashboards, and AI models—can trust the data they are working with.
• Collaborate closely with the Business Analyst and IT AI & Data Manager to understand reporting and analytics requirements, translating them into efficient, well-documented data solutions.
• Design and implement AI-augmented data engineering solutions—including AI agents, automated data quality processes, and intelligent pipeline components—that drive efficiency across Continental’s data platform.
• Build and optimize semantic models and datasets that power PowerBI dashboards and reports, working with analysts to ensure visualizations are backed by performant, accurate data.
• Contribute to data governance efforts by maintaining data dictionaries, pipeline documentation, and lineage tracking so the team can understand, trust, and build on the work being done.
• Maintain pipeline health, troubleshoot data failures, and proactively address issues before they impact reporting or downstream business processes.
• Stay current on developments in cloud data engineering, AI tooling, and the Microsoft data ecosystem, bringing new ideas and approaches to the team as the platform continues to mature.
• Support M&A and new business onboarding by connecting new source systems into existing pipelines and data models with minimal disruption to existing operations.

Requirements

• Bachelor’s degree in Computer Science, Information Systems, Data Engineering, Mathematics, or a related technical field.
• 3–5 years of experience in data engineering, with a portfolio of work that includes building and maintaining production-grade data pipelines and data models.
• Hands-on experience with Microsoft Azure data services and Microsoft Fabric—including Data Factory, Azure Functions, Synapse, and Lakehouse—as the primary stack.
• Experience with AWS data services is also relevant and viewed favorably, particularly where cloud architecture patterns are transferable.
• Strong SQL skills with experience writing complex queries, stored procedures, and transformations for large, multi-source datasets.
• Strong proficiency in Python for data engineering work—including pipeline development, data transformation, automation scripting, and cloud function development.
• Familiarity with other modern scripting languages or frameworks used in cloud-native data environments is a plus.
• Experience building semantic models or datasets that serve as the foundation for Power BI reporting, with an understanding of how modeling decisions affect performance and usability.
• Demonstrated hands-on experience applying AI within a data engineering context—implementing AI agents, integrating LLM-based processes into pipelines, or building AI-driven automation.
• A solid understanding of data quality, data governance, and data lineage principles, and experience applying them in a real production environment.
• Experience integrating data from multiple enterprise systems (ERP, POS, HRIS, CRM, or similar), with an ability to navigate source system complexity and build reliable connections between them.
• Strong communication skills with the ability to work alongside business analysts and non-technical stakeholders to understand requirements and explain technical decisions clearly.
• A curiosity-driven, growth-oriented mindset—someone who keeps up with the evolving data and AI landscape, takes ownership of their work end-to-end, and is always looking to improve how things are built.

Qualifications

• Technical skills: Strong SQL, Python, Azure/Amazon cloud services, data modeling, data quality, data lineage, AI integration.
• Problem-solving: Ability to design and implement scalable data pipelines, models, and processes.
• Collaboration: Strong collaboration with business analysts, IT teams, and AI managers.
• Communication: Clear communication skills with non-technical stakeholders.
• Adaptability: Continuous learning and staying updated with the latest data and AI tools.