Principal Orchestration Engineer

Summary

Motion Industries is seeking a Principal Orchestration Engineer to serve as a technical leader for our enterprise Databricks platform and the next generation of data and intelligent applications built on it.

This role will combine deep Databricks platform expertise with hands-on engineering and architecture. You will help design, administer, govern, and evolve the Databricks environment while establishing the engineering patterns that enable Enterprise Intelligence teams to build reliable data products, analytics solutions, AI/BI Genie experiences, and AI agents at enterprise scale.

This is not simply a Databricks developer or administrator role. You will own technical patterns across the platform, from workspace architecture, security, governance, deployment, and workload orchestration through the emerging AI capabilities that consume enterprise data.

As our capabilities mature, you will play a leading role in extending the platform into conversational analytics and agentic AI using Databricks AI/BI Genie, Mosaic AI, large language models, and related technologies.

This is a hands-on technical leadership role. You will personally engineer solutions while establishing the standards, reference architectures, frameworks, and best practices that allow the broader Enterprise Intelligence organization to scale.

What You'll Actually Do

Databricks Platform Engineering & Architecture

• Serve as a principal technical authority for the Databricks platform within Enterprise Intelligence.
• Design and evolve the architecture of our Databricks environment across development, testing, and production workloads.
• Establish scalable patterns for workspace configuration, compute, SQL warehouses, jobs, workflows, security, networking, and platform integrations.
• Design and implement governance and access-control patterns using Unity Catalog.
• Establish platform standards for identity, permissions, service principals, secrets, and secure access to enterprise data.
• Develop repeatable deployment and configuration patterns using infrastructure-as-code and CI/CD practices.
• Partner with cloud, security, DevOps, and data engineering teams to ensure the platform meets enterprise requirements for reliability, security, performance, and supportability.
• Monitor and optimize platform performance, compute utilization, reliability, and cost.

Data & Workload Orchestration

• Architect reusable orchestration patterns for data pipelines, analytics workloads, intelligent applications, and automated processes.
• Build and maintain Databricks Jobs and Workflows for production workloads.
• Create standardized engineering patterns that move solutions consistently from development through production.
• Develop reusable libraries, frameworks, templates, and automation that improve engineering velocity across the Enterprise Intelligence organization.
• Partner with data engineers to establish reliable patterns for ingestion, transformation, testing, deployment, monitoring, and recovery.
• Integrate Databricks workloads with Azure services, enterprise applications, APIs, and other data platforms.
• Establish observability and operational standards that allow teams to quickly identify and resolve production issues.

Data Governance & Platform Standards

• Implement scalable data governance patterns using Unity Catalog and related Databricks capabilities.
• Help establish standards for data access, lineage, metadata, data quality, security, and environment separation.
• Design platform guardrails that enable teams to move quickly while maintaining appropriate governance and controls.
• Partner with data product, security, architecture, and business teams to translate enterprise governance requirements into practical engineering patterns.
• Establish reference architectures and technical standards for common platform use cases.

Genie & Intelligent Applications

As Enterprise Intelligence expands its AI capabilities, this role will help establish the technical foundation for intelligent applications built on Databricks.

• Design and develop Databricks AI/BI Genie experiences grounded in governed enterprise data.
• Establish patterns for designing data products and semantic context that allow Genie and other AI experiences to accurately understand enterprise data.
• Build and integrate AI agents that securely interact with governed data, business logic, APIs, and enterprise systems.
• Develop retrieval and grounding patterns for applications that require enterprise context.
• Establish evaluation, monitoring, tracing, security, and governance practices for production AI applications.
• Explore and implement capabilities within the Databricks Mosaic AI ecosystem as Enterprise Intelligence's agentic architecture matures.
• Establish reusable patterns that allow other engineers to safely develop intelligent applications without rebuilding foundational platform capabilities.

The goal is not AI experimentation for its own sake. The focus is creating governed, production-quality capabilities that solve meaningful business problems.

Key Responsibilities

Databricks Platform & Architecture | ~35%

• Architect and administer the enterprise Databricks environment.
• Establish standards for workspaces, compute, Unity Catalog, security, networking, and platform configuration.
• Implement automated deployment and environment-management practices.
• Own technical patterns for platform reliability, monitoring, performance, and cost management.
• Evaluate emerging Databricks capabilities and determine how they fit within the Enterprise Intelligence architecture.

Orchestration & Production Engineering | ~25%

• Design production-grade orchestration patterns for data and application workloads.
• Build reusable frameworks and automation for Databricks Jobs and Workflows.
• Establish CI/CD, automated testing, deployment, monitoring, and recovery patterns.
• Integrate Databricks with Azure and enterprise platforms.
• Improve developer experience through reusable libraries, templates, and tooling.

Genie & AI Agent Engineering | ~25%

• Design and develop AI/BI Genie experiences.
• Establish data and semantic patterns supporting natural-language analytics.
• Build AI agents that interact securely with enterprise data and systems.
• Design retrieval, grounding, evaluation, monitoring, and guardrail patterns.
• Help define the architecture for Enterprise Intelligence's emerging agentic capabilities.

Technical Leadership & Enablement | ~15%

• Establish platform engineering standards and reference architectures.
• Lead technical design and architecture reviews.
• Mentor data engineers, analytics engineers, and other technical team members.
• Develop documentation, reference implementations, and engineering best practices.
• Partner with Enterprise Intelligence leadership on platform roadmap and capability development.
• Evaluate new Databricks and AI capabilities and translate them into pragmatic enterprise architecture decisions.

Required Qualifications

Databricks Platform Expertise

• Deep hands-on experience designing, implementing, and operating Databricks in an enterprise environment.
• Experience administering Databricks environments rather than solely developing notebooks or pipelines within an existing platform.
• Strong experience with Databricks workspace architecture and configuration.
• Hands-on experience with Unity Catalog, including governance and access-control patterns.
• Experience designing and operating Databricks Jobs and Workflows.
• Experience establishing development, testing, and production deployment patterns.
• Strong understanding of Databricks compute and SQL workload architecture.
• Experience implementing CI/CD and infrastructure automation for Databricks environments.
• Demonstrated ability to troubleshoot platform, workload, performance, and reliability issues.

Software & Data Engineering

• Expert-level Python.
• Strong SQL and data-querying skills.
• Strong understanding of software engineering fundamentals including source control, automated testing, CI/CD, code review, and modular application design.
• Experience designing APIs and integrating enterprise systems.
• Deep understanding of modern data engineering patterns including ingestion, ETL/ELT, transformation, orchestration, and data modeling.
• Experience building production-grade data pipelines.
• Strong understanding of data governance, security, lineage, observability, and data-quality principles.

Cloud & DevOps

• Strong experience working with Microsoft Azure or comparable enterprise cloud environments.
• Experience with infrastructure-as-code and automated environment configuration.
• Understanding of identity and access management, networking, secrets management, and cloud security.
• Experience implementing monitoring and observability for production systems.
• Strong understanding of performance and cloud-cost optimization.

AI & Intelligent Applications

Experience in this area is strongly preferred, with the expectation that the role will become increasingly involved in AI engineering as the capability matures.

• Experience building or configuring Databricks AI/BI Genie spaces.
• Familiarity with Databricks Mosaic AI capabilities.
• Experience developing applications using large language models or LLM APIs.
• Understanding of retrieval-augmented generation, semantic search, embeddings, and grounding patterns.
• Familiarity with AI agent architectures and orchestration frameworks.
• Understanding of AI evaluation, observability, security, and guardrail concepts.

Deep Databricks platform expertise is more important to this role than experience with any single AI framework.

Experience & Background

• 8+ years in software engineering, data engineering, platform engineering, cloud engineering, or related technical roles.
• Significant hands-on experience designing and operating enterprise data platforms.
• Demonstrated depth with Databricks in production environments.
• 3+ years operating in a senior, lead, staff, principal, or comparable technical leadership capacity.
• Proven ability to take complex technical capabilities from architecture through production.
• Experience establishing engineering standards and reusable technical patterns across teams.
• Ability to work across data engineering, analytics, cloud infrastructure, security, DevOps, architecture, and business teams.
• Proven ability to translate business requirements into scalable technical solutions.
• Ability to lead through technical influence, mentoring, architecture, and hands-on engineering rather than direct people management.

LICENSES & CERTIFICATIONS: None required.

SUPERVISORY RESPONSIBILITY: No Supervisory Responsibility

BUDGET RESPONSIBILITY: No

COMPANY INFORMATION:

Motion offers an excellent benefits package which includes options for healthcare coverage, 401(k), tuition reimbursement, vacation, sick, and holiday pay.

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