Senior Data Engineer

Insight Global•Dunwoody, GA

1d•Onsite

About The Position

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).

Requirements

5+ years of experience in Data Engineering or Data Architecture
2+ years of experience with developing with Databricks
Strong expertise in data modeling concepts (conceptual, logical, physical; dimensional and domain‑oriented models).
Hands‑on experience designing solutions on Databricks or modern cloud data platforms using the Medalion architecture.
Deep knowledge of SQL and strong understanding of how data models impact performance and usability.
Experience designing data structures that support BI tools (Power BI) and advanced analytics.
Proven experience implementing enterprise data governance controls through architecture across multiple systems of record (authoritative source designations, canonical models, data contracts, and standard integration patterns) for production use.
Strong communication skills and ability to influence across technical and business stakeholders.

Nice To Haves

Experience with Databricks Unity Catalog, lineage, and governance tooling.
Familiarity with AI / ML‑driven analytics and GenAI data requirements.
Experience working in a global, distributed team model.
Exposure to data mesh, domain‑oriented ownership, or product‑based data architectures.
Background in enterprise modernization or large‑scale legacy platform migrations.

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.