Join a technology-driven environment where data architecture and engineering play a central role in enabling advanced analytics, streaming, and artificial intelligence initiatives. As a Senior Data Engineer, you will design and develop scalable, secure, and high-performance data platforms using Microsoft Azure and Databricks. You will lead the implementation of Lakehouse architectures, build robust data pipelines, and ensure information remains reliable, accessible, and well-governed. Working closely with technical stakeholders and international teams, you will help transform complex data requirements into efficient, production-ready solutions. Your expertise will contribute to improving data quality, optimizing platform performance, and enabling data-driven decision-making across the organization. This fully remote, long-term opportunity is ideal for a senior professional who combines deep technical expertise with strong communication skills and the ability to collaborate confidently with clients and teams across North America.
Data Architecture and Solution Design: Design, develop, and maintain scalable data architectures using Microsoft Azure and Databricks, ensuring alignment with business objectives, technical requirements, and long-term platform needs.
Lakehouse Architecture Implementation: Build and optimize modern Lakehouse solutions using Delta Lake and Unity Catalog to support reliable data storage, processing, access management, and analytics.
Data Pipeline Development: Design, implement, maintain, and optimize robust data pipelines that support efficient ingestion, transformation, integration, and delivery of data across multiple sources.
Advanced Data Processing: Leverage Apache Spark and PySpark to develop high-performance data processing solutions capable of handling large volumes of structured and unstructured information.
Streaming and Real-Time Data: Support the development and optimization of streaming data architectures and processing workflows to enable timely insights and data-driven applications.
Data Quality and Reliability: Establish and implement data quality controls, validation processes, and monitoring mechanisms to ensure accuracy, consistency, completeness, and reliability across data platforms.
Data Governance and Security: Implement and maintain data governance frameworks, access controls, security policies, and metadata management practices to protect sensitive information and support compliance requirements.
Platform Performance and Optimization: Monitor and optimize data workloads, infrastructure utilization, processing performance, and platform costs to ensure scalable and efficient operations.
Analytics and AI Enablement: Develop and maintain data foundations that support business intelligence, advanced analytics, machine learning, and artificial intelligence initiatives.
Technical Collaboration: Work closely with engineering teams, analytics specialists, business stakeholders, and international clients to translate requirements into effective technical solutions.
Technical Documentation and Best Practices: Maintain architecture documentation, establish engineering standards, and promote best practices in data modeling, pipeline development, governance, and platform management.
International Stakeholder Communication: Communicate technical concepts, architectural decisions, project progress, and recommendations clearly in English when collaborating with clients and teams in the United States and Canada.