Lead Data Engineer

Position Description We are seeking a Lead Data Engineer to architect, build, and lead the development of scalable, cloud-based data platforms that support enterprise analytics, operational reporting, and advanced data use cases. This role provides technical leadership in designing and optimizing ETL/ELT frameworks using Azure data services (Fabric, Data Lake, Data Factory), integrating data from ERP, CRM, and operational systems, and establishing robust data models within a modern Lakehouse architecture. The ideal candidate brings deep SQL and Python expertise, extensive experience with distributed data platforms, and strong knowledge of data architecture, governance, and performance optimization. This individual will serve as a technical leader and mentor, partnering closely with architects, analysts, application teams, and business stakeholders to deliver reliable, scalable, and well-governed enterprise data solutions. Responsibilities Lead the design, development, and optimization of scalable data pipelines supporting ingestion, transformation, and enterprise-wide data consumption. Architect and implement enterprise-grade ETL/ELT frameworks using Azure Fabric or comparable cloud data platforms. Oversee and optimize data integrations from ERP (NetSuite/SAP), CRM (Salesforce), internal systems, APIs, and third-party data sources. Design and govern high-quality, scalable data models supporting analytics, reporting, operational systems, and advanced use cases. Partner with Data Architects to define and implement Lakehouse patterns, Delta Lake strategies, medallion architecture, and domain-driven design principles. Establish and enforce data quality frameworks, validation standards, lineage tracking, and observability practices. Drive performance optimization, scalability, reliability, and cost governance across cloud environments. Provide technical leadership and mentorship to data engineers; conduct design reviews and enforce engineering best practices. Collaborate cross-functionally with analysts, application teams, and business stakeholders to translate requirements into scalable data solutions. Lead MDM, metadata management, governance, and data standardization initiatives. Oversee CI/CD automation, DevOps integration, testing frameworks, and monitoring strategies for data workflows. Evaluate emerging technologies and recommend platform improvements aligned with enterprise strategy. Minimum Qualifications 10+ years of experience in data engineering, data architecture, or related roles. Proven experience leading large-scale data platform initiatives in cloud environments. Extensive hands‑on experience with Azure data services (Data Lake, Data Factory, Fabric, Synapse, or similar). Advanced proficiency in SQL and Python; experience with Spark or distributed processing frameworks. Deep experience designing and implementing enterprise ETL/ELT frameworks. Strong expertise in data modeling (dimensional modeling, star schema, lakehouse/Delta modeling). Experience integrating complex enterprise systems (ERP, CRM, operational platforms). Strong understanding of data governance, metadata management, MDM, and data quality frameworks. Experience with performance tuning, workload optimization, and cloud cost management. Demonstrated ability to lead technical teams, conduct architecture reviews, and mentor engineers. Strong problem‑solving, debugging, and system design skills. Travel may be required up to 5%, depending on business needs. Preferred Qualifications Experience with Delta Tables, Snowflake, Synapse, or comparable cloud data platforms. Experience with event-driven and streaming architectures (Kafka, Event Hub, streaming pipelines). Familiarity with finance, operations, energy, or ERP-driven data domains. Experience designing API-based data integrations and modern integration patterns. Azure certifications (Data Engineer Associate, Solutions Architect, or equivalent). Experience enabling analytics teams, data science workflows, or ML pipelines. Experience implementing enterprise data security and compliance frameworks. Use of AI Tools As a technology organization, Qcells expects team members to leverage AI models and AI-assisted tools in their daily workflows where appropriate. Candidates should be comfortable working in an AI-augmented environment and applying sound judgment when using AI-generated outputs. During the interview process, candidates will be asked to share examples of how they have used AI tools or models in their work. Physical, Mental & Environmental Demands Mobility: Standing 20% of time, Sitting 70% of time, Walking 10% of time. Strength: Pulling up to 10 Pounds, Pushing up to 10 Pounds, Carrying up to 10 Pounds, Lifting up to 10 Pounds. Dexterity: Typing F, Handling F, Reaching F. Agility: Turning F, Twisting F, Bending O, Crouching O, Balancing N, Climbing N, Crawling N, Kneeling N. #J-18808-Ljbffr Qcells North America