Senior Data Engineer

The Data & Insights organization at Insight Global is responsible for delivering Trusted Data & Verified Insights to power decision‑making, AI, BI, and self‑service analytics across the enterprise. As a Senior Data Engineer, you will play a critical role in Insight Global's data modernization initiative , helping lead design and hands-on implementation of our next-generation data platform in Databricks. You will help design and build scalable, governed, and performant data models across multiple business domains, ensuring they enable Business Intelligence, AI/ML, and self-service analytics at enterprise scale.   This role combines senior-level technical leadership with execution—setting the standard for how data engineering work is designed, built, tested, deployed, and supported. As a senior Data engineer, you will develop reusable frameworks, delivery patterns, and engineering practices that improve quality, consistency, performance, and maintainability across data products, pipelines, and medallion-layer models. You will mentor other engineers, drive delivery with AI, and help turn strategy and solution direction into reliable, production-ready data engineering solutions. This role reports into the Data Engineering and works in close partnership with Data Architecture, Operations, and Data Strategy & Governance teams based out of the Atlanta office (HQ).

Key Responsibilities Design, build, test, deploy, and support scalable data engineering solutions primarily on Azure Databricks. Set the engineering standard for reliable, maintainable, performant, and well-governed data pipelines, data products, and medallion-layer models. Develop reusable frameworks, tooling, delivery patterns, and engineering practices that improve quality, consistency, efficiency, performance, and maintainability. Translate solution direction and business requirements into production-ready data engineering implementations. Build well-structured, reusable data models and pipelines that support Power BI reporting, AI/ML use cases, GenAI enablement, and self-service analytics. Modernize legacy data pipelines and processes as part of the migration to Databricks, supporting both greenfield development and legacy platform modernization. Apply automated testing, CI/CD, code review, and release management practices to improve delivery quality and execution. Optimize data structures, batch and streaming patterns, compute usage, pre-aggregation, and performance strategies for cost-effective Databricks delivery. Partner with Data Architecture, Data Strategy & Governance, BI, and Data Operations teams to align engineering implementations with enterprise standards, governance requirements, and supportability expectations. Implement governance-aware engineering practices including lineage, metadata, data ownership, policy-driven access, auditability, and standardized data contracts. Drive semantic consistency across domains by supporting standardized business definitions, KPI logic, metric calculations, dimensions, grain, and conformance rules. Leverage AI-assisted development practices and tools, including GitHub Copilot and Databricks capabilities, to improve delivery speed and engineering effectiveness. Provide technical guidance, mentoring, feedback, and code/design review for Data Engineers while encouraging innovation, collaboration, and best practices. Technical Focus Databricks‑first architecture (Lakehouse, Medallion layers: Bronze / Silver / Gold) Enterprise and domain‑driven data modeling Data structures optimized for AI / ML and self‑service consumption Governance‑aware design (lineage, catalogs, metadata, ownership) Performance‑aware modeling and pre‑aggregation strategies, and Development Supporting both greenfield and legacy modernization use cases Architectural definitions intended to be operationalized through shared platforms and engineering automation Qualifications Required Qualifications 5+ years of