Own analytics data architecture, including data transform, modeling, and serving layers.
Partner with Data Governance and IT Architecture to define and enforce data modeling standards (e.g., dimensional, semantic, or metric layers) to support self-service analytics and consistent metrics.
Lead architectural decisions around cloud data warehouses and ML orchestration frameworks.
Partner with analytics and business teams to ensure data platform is usable, trusted, and performant, not just technically elegant.
Establish technical best practices for data quality, lineage, metadata, and governance in collaboration with data governance team.
Design and operate the ML/AI platform supporting the full model lifecycle (experimentation, training, validation, deployment, and monitoring) in partnership with data science and engineering teams.
Determine the need and design of feature engineering stores to reduce friction from research to production.
Design and develop framework for model versioning & end-to-end reproducibility
Build and operate a CI/CD for ML/AI solution that enables model deployment & monitoring into production systems at scale.
Collaborate with model governance, cyber security & architecture, privacy, cloud architecture and other stakeholders to maintain enterprise wide MLOps standards
Set the technical vision and roadmap for analytics and ML platforms aligned to business strategy.
Make clear trade-offs between build vs. buy, speed vs. scale, and experimentation vs. operational rigor.
Lead architecture reviews and provide technical guidance on complex initiatives within the data and ML platforms.
Stay current on evolving data and ML platform technologies and assess relevance pragmatically.
Lead and mentor senior data engineers, analytics engineers, and ML platform engineers.
Establish clear technical standards, documentation, and operational practices for the data and ML platforms.
Collaborate with product, engineering, analytics, security, and infrastructure teams to ensure platform alignment and reliability.
Influence without authority across teams that depend on the data and ML platform.

Requirements

Proven experience designing and operating modern analytics data platforms at scale.
Hands-on experience with production ML systems and MLOps.
Strong architectural judgment across data storage, compute, orchestration, and deployment patterns.
Experience with the major cloud platforms, preferred experience with Azure
Experience leading senior technical contributors and setting technical standards.
Ability to translate business and analytical needs into durable technical solutions.
Takes on ownership mentality and always pushes for continuous improvement.
Inspires the team through strong leadership, coaching, and mentoring.
Willing to go the extra mile as a manager with frequent check-ins, valuable feedback, and rigorous performance management.
Experience supporting both BI/analytics workloads and near-real-time ML use cases.
Familiarity with cloud-native architectures and infrastructure-as-code.
Experience enabling self-service analytics and ML for non-platform teams.
Background in regulated or data-sensitive environments.
Bachelor’s Degree in the field of Computer Science/Engineering, Analytics, Mathematics, or related discipline required
Master’s Degree in the field of Computer Science/Engineering, Analytics, Mathematics, or related discipline preferred
7-10 years of experience in data engineering, analytics platforms, ML infrastructure, or related roles required
5-7 years of experience leading technical teams in data engineering, ML engineering or related fields required

Benefits

401K matching
bonding leave for new parents (12 weeks, 100% paid)
tuition assistance
training
GM employee auto discount
community service pay
nine company holidays

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data architecturedata modelingcloud data warehousesML orchestration frameworksfeature engineeringmodel versioningCI/CD for ML/AIdata qualitymetadata governanceproduction ML systems

Soft Skills

leadershipmentoringcollaborationtechnical judgmentownership mentalitycontinuous improvementinfluence without authoritycoachingcommunicationperformance management

Certifications

Bachelor’s Degree in Computer ScienceBachelor’s Degree in EngineeringBachelor’s Degree in AnalyticsBachelor’s Degree in MathematicsMaster’s Degree in Computer ScienceMaster’s Degree in EngineeringMaster’s Degree in AnalyticsMaster’s Degree in Mathematics