Manager Data Engineering
Overview
Lead a high-performing cross-functional data engineering team and drive AI-assisted development practices. Own the end-to-end data pipelines and analytics infrastructure for floor plan lending operations while mentoring engineers and shaping technical direction.
What You'll Do8
- 1Build and lead a high-performing cross-functional data engineering team mentoring engineers and establishing growth paths into AI-augmented engineering practices
- 2Oversee the full software development lifecycle for data pipelines transformations and analytics services running in Azure AWS and Snowflake
- 3Ensure delivery of secure well-tested scalable data systems aligned to NextGear's Domain Constitution and Target State Architecture
- 4Champion spec-driven development workflows with detailed Change Specs authored collaboratively before code generation and validation against architectural guardrails
- 5Maintain and contribute to component constitutions and context packs that guide AI-assisted code generation in team repositories
- 6Partner with product managers architects and platform teams to deliver against NextGear's roadmap and coordinate across the Release Train to keep team patterns aligned with shared SDD standards
- 7Drive experimentation with AI coding workflows and surface effective patterns and anti-patterns back to the broader engineering organization
- 8Drive experimentation with AI coding workflows and surface effective patterns and anti-patterns back to the broader engineering organization
Requirements15
- 1Minimum Bachelor’s degree in a related discipline and 6 years’ experience in a related field
- 21+ year of experience in management or lead role
- 3Hands-on experience with AI coding assistants as a primary part of the development workflow
- 4Demonstrated experience with spec-driven or design-doc-gated development workflows
- 5Experience reviewing AI-generated code and implementation plans for quality security and architectural compliance
- 6Availability for rotational on-call duties outside of standard business hours
- 7Preferred hands-on experience in Azure cloud environments including Azure Functions and Azure-based data platform services
- 8Experience with Microsoft Fabric for data pipelines and transformation including Fabric-based ETL/FTP integration patterns
- 9Familiarity with AWS services commonly used for data workloads such as AppFlow S3 Lambda Fargate EC2
- 10Direct data engineering experience with data modeling SQL SQL Server dbt and data governance practices
- 11Experience with Snowflake and modern cloud data warehousing
- 12Experience with Power BI or comparable analytics platforms
- 13Experience in automotive finance lending or risk management domains
- 14Experience creating or governing component-level documentation artifacts such as constitutions context packs architecture overviews and operational runbooks
- 15Experience with CI/CD pipelines and infrastructure as code using Terraform CloudFormation Bicep or comparable tools
Salary Insight
Salary not disclosed in listing
Similar open positions
Explore active roles that match your skills and interests.
Aberdeen Proving Ground Federal Credit Union
VerifiedVP Emerging Technologies, Data & AI Strategy
Lead the credit union's enterprise data, AI, and software development programs, driving scalable, secure solutions that empower data-driven decisions. Oversee a team of engineers and data professionals, owning strategy from roadmap to delivery. Partner with the CIO and business leaders to pilot emerging tech and modernize member experiences. This role blends hands-on Microsoft SQL expertise with strategic leadership, positioning you as a key technology visionary in a regulated financial environment.
General Motors
VerifiedData Engineering Manager General Motors Austin Texas
Lead a team of data engineers building Customer data platforms for OnStar Connected Services Customer Marketing Personalization AI Advanced Analytics. This technical leadership role builds scalable cloud-native data products across multi-cloud Lakehouse architecture focusing on Databricks Distributed Data Processing near Real-Time Streaming AI ML Generative AI. Seeking technology leader combining deep technical expertise with people leadership.
EXTEU-AC-CareerSite
VerifiedPrincipal Data Architect, Data Engineering & AI
You will own the enterprise data architecture and strategy, enabling scalable AI/ML, telemetry analytics, and business intelligence across a global organization. You will lead the design of cloud-native data platforms on AWS and Azure, from ingestion to insights, and set the blueprint for data storage, integration, and governance. Collaborating with Data Science and AI teams, you will industrialize ML pipelines and drive data as a strategic asset. This role combines deep technical leadership with executive influence, shaping the future of data-driven decision making.
US031 Marsh & McLennan Shared Services LLC
VerifiedEnterprise Data Engineering & Analytics Director
Lead and scale enterprise data platform and analytics capabilities. Build trusted governed scalable data foundations that power analytics reporting and AI. Foster high performing inclusive culture. Drive modern cloud data platforms creating measurable business value.
Nava Software Solutions
VerifiedManager AI Engineering Nava Software Solutions
Lead AI Engineering initiatives in Denver CO overseeing a team of professionals to deliver innovative solutions. Drive technical excellence and innovation across projects. This role offers unique opportunities to shape cutting edge AI systems.
JPMorganChase
VerifiedSr Lead Software Engineer, Data Engineer
You will own the design and delivery of enterprise data platforms and pipelines for Securities Services Technology, serving millions of transactions. 5+ years of applied experience with Python, Java, and Spark positions you to lead an agile team in building secure, scalable products. You will define architecture, implement semantic layers with Agentic AI, and integrate data across AWS and Databricks. This role stands apart through its focus on agentic metadata mining and semantic modeling, driving firmwide innovation.