Position Purpose Lead TAIT's Enterprise Data, Integration and AI Engineering organization, responsible for enterprise applications, data platforms, cloud engineering, system integrations, analytics and AI enablement. This hands-on technology leader drives delivery across Databricks, AWS, MuleSoft, data engineering, application development and enterprise architecture while building scalable platforms, modernizing legacy systems and enabling business growth through trusted data and governed AI solutions.
Responsibilities Lead people and enterprise IT engineering
- Own the roadmap delivery with the VP, Enterprise Applications and Transformation Architect, prioritizing business value, risk, dependencies, cost and capacity. Coach teams and partners; establish standards, accountability, career development and knowledge sharing.
- Contribute directly to architecture, code, reviews, pipelines, integrations and troubleshooting. Partner with business, application, Cybersecurity, Infrastructure and change leaders to deliver maintainable solutions and reduce key-person dependency.
- Lead multidisciplinary teams across software engineering, data engineering, analytics, cloud engineering, enterprise integration and AI solutions.
Build modular platforms and integrations
- Use commercial platforms for transactions and controls, favoring configuration over customization. Build TAIT differentiators through loosely coupled services, common enterprise objects and governed data exchange.
- Lead MuleSoft adapters, custom connectors and orchestration (directors), separating system mappings from business logic. Require authenticated APIs, semantic versioning, events, idempotency, retries, reconciliation, monitoring, mocks and contract tests; expose approved capabilities through Model Context Protocol (MCP).
- Establish enterprise integration architecture standards supporting ERP, CRM, PLM, HCM and operational systems using API-led connectivity and event-driven architecture principles.
Review and modernize legacy custom application
- Assess legacy custom applications, scripts and integrations with business owners to confirm business value, usage, dependencies, security, supportability and cost. Prioritize what to retain, replace with commercial capabilities, modernize or retire; preserve valuable business logic and institutional knowledge
- Modernize and redeploy selected solutions using current platforms, supported frameworks, cloud services and AI-assisted engineering where appropriate. Refactor into maintainable components with governed APIs and data access, automated testing, CI/CD, monitoring and documentation; validate business outcomes and data reconciliation before transition, with clear ownership, training and rollback plans.
- Drive application modernization and digital transformation initiatives that improve scalability, reliability, user experience and operational efficiency.
Curate trusted enterprise data
- Mature Databricks Lakehouse Platform on AWS, leveraging Delta Lake, Unity Catalog, Bronze raw data, Silver cleaned and historical data, and certified Gold products for projects, labor, finance and operations. Build batch, change-data-capture and streaming pipelines; define common objects, authoritative sources and metrics with business owners.
- Lead governance with data owners and stewards. Align Unity Catalog with existing Microsoft Purview catalogs and glossaries; maintain classifications, source-to-report lineage, access approvals, quality checks, remediation ownership and retention, archiving, backup and secure destruction controls.
Deliver analytics and governed AI
- Migrate SQL warehouse, ETL and Power BI workloads to Databricks through reconciled phases. Enable employee self-service analysis, dashboards and ChatGPT/Claude experiences over certified Gold data and shared semantic models; preserve permissions, traceability and consistent business definitions.
- Build reusable TAIT Intelligence Layer services using retrieval-augmented generation (RAG), governed tools and agentic orchestration for quoting, engineering reuse, project forecasting and operational exceptions. Turn prototypes into supported products with ownership, service levels, release criteria and measurable value.
- Lead enterprise adoption of Artificial Intelligence (AI), Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) and intelligent automation solutions to improve productivity and decision-making.
- Engineer AI for security, privacy and resilience: least-privilege agent identities, segregation of duties, approved vendor/data-use terms, prompt-injection defenses, human approvals, audit evidence, tested recovery and non-AI fallback. Version prompts/models; evaluate accuracy, latency, cost and drift. Keep authoritative posting in commercial systems.
Lead migration and reliable delivery
- Publish a systems and people transition plan covering dependencies, delivery waves, training, hiring and partners. Plan coexistence, reconciled migrations, cutover rehearsals and rollback; retire temporary bridges, redundant reports and agreed legacy/Autodesk dependencies.
- Support ERP, PLM, MES, rental and HCM/payroll modernization and acquisitions through reusable interfaces. Develop SQL, BI and application specialists for cloud data, integration and AI; enable senior engineers to supervise partner delivery.
- Balance quality and speed through Jira, small releases, acceptance criteria, code review, automated testing and CI/CD. Own monitoring, incidents, runbooks and recovery; manage technical debt and cloud/AI costs. Track lead time, defects, data freshness, adoption and verified benefits.
Management Responsibility Directly manage Engineering, Data and Analytics employees and specialist partners, with accountability for hiring, performance, development, succession, capacity and delegated budgets.
Position Requirements - Bachelor's degree in computer science, engineering, information systems or a related field, or equivalent practical experience;
- 8+ years of progressive experience in software engineering, data engineering, enterprise applications, cloud platforms or systems integration, including 3+ years leading technical teams, managers, or cross-functional engineering organizations.
- Hands-on expertise with Python, SQL, Databricks Lakehouse Platform, cloud-native data engineering and enterprise analytics solutions; AWS cloud architecture including IAM, S3, networking, orchestration, observability and performance optimization.
- MuleSoft Anypoint or similar, DataWeave, REST/JSON, event-based integration and secure API lifecycle management and enterprise integration patterns; Git, Jira, DevOps, automated testing, infrastructure as code and CI/CD.
- Successful data platform modernization, cloud migration, Power BI modernization, analytics transformation and production AI/Generative AI deployments, including governance, model evaluation and operational support; experience leading workforce development, organizational change, technology transitions and strategic partners.
- Must possess the right to work in the US.
Preferred Experience - Global project-based or engineer-to-order manufacturing; integrations across ERP, Salesforce, Kantata/PSA, Autodesk Vault/Fusion Manage, MES/EAM, rental, HCM/payroll and collaboration platforms.
- Purview governance, master data, semantic models, full-stack/quoting applications, enterprise ChatGPT/Claude, identity federation, FinOps or M&A integration. Relevant Databricks, AWS or MuleSoft certifications.
TAIT Benefits: - Competitive medical, dental, and vision insurance plans
- Health Savings Account (HSA) with employer contributions (HDHP plans)
- Flexible Spending Accounts (Medical & Dependent Care)
- Employer-paid life insurance with optional supplemental coverage
- Employer-paid Short-term with optional long-term disability insurance
- 401(k) retirement plan with company match
- Paid parental leave
- Employee Assistance Program (EAP)
- Wellness stipends
- Voluntary benefits (e.g., accident, critical illness
- Paid vacation and 12 holidays
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