Lead operational reliability and platform enablement for Databricks: build monitoring, CI/CD, deployment standards, compute and job policies, observability, runbooks, and governance to support secure, cost-aware, production data workloads across regulated environments. Mentor engineers and align platform with cloud/infrastructure and compliance requirements.
Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube.
The Sr. Staff Platform / Data Reliability Engineer is responsible for making Databricks a reliable, secure, scalable, and operationally mature platform for enterprise use. This role focuses on the platform itself rather than domain-specific business modeling, ensuring the lakehouse can support multiple domains, regulated data handling, and a growing set of production workloads without becoming fragile, expensive, or hard to govern.
This role partners closely with the Senior Staff Data Engineer, who focuses more heavily on ingestion and medallion implementation patterns, and with the existing Cloud & Infrastructure team, which owns cloud accounts, networking, and foundational infrastructure. The Platform / Data Reliability Engineer owns the Databricks operational layer that sits above that foundation: reliability, observability, deployment standards, compute and job policies, and platform enablement patterns for internal users.
What you'll do:
- Own operational excellence for the Databricks platform, including monitoring, alerting, observability, incident response support, and production runbook patterns for data jobs and platform services.
- Define and maintain CI/CD and promotion standards for Databricks assets, including workflows, jobs, notebooks, code packages, infrastructure configuration, and environment promotion from dev to prod.
- Design and maintain platform standards for job orchestration, cluster and compute policies, service principal usage, environment isolation, and production execution reliability.
- Establish reusable operational templates and enablement patterns for new domains onboarding to Databricks, including logging conventions, job tagging, metadata capture, and support handoff expectations.
- Partner with the Senior Data Engineer to ensure ingestion and medallion patterns are implemented in a way that is observable, recoverable, cost-aware, and secure in production.
- Work with the cloud/infrastructure team to align Databricks configuration and usage patterns with broader enterprise cloud standards, especially where commercial and future government-hosted environments are involved.
- Help enforce technical controls for data segregation, access boundaries, and operational compliance in a highly regulated environment.
- Track and improve platform health metrics such as job success rates, incident trends, data pipeline reliability, cost efficiency, and environment drift.
- Document platform standards, operational expectations, and support models so the Databricks platform can scale beyond a small founding team.
- Mentor internal engineers who are growing into platform responsibilities, helping expand Databricks operational knowledge within the team.
Required qualifications:
- 12+ years of relevant experience in data platform engineering, platform operations, site reliability engineering, or modern cloud data infrastructure.
- Hands-on experience with Databricks or a closely related cloud data platform in production environments.
- Experience designing or operating CI/CD, environment promotion, version control, and deployment automation for data platforms and pipelines.
- Strong understanding of platform operations concepts such as observability, monitoring, alerting, incident management, and reliability engineering.
- Experience with compute policy design, workload isolation, service principals, and secure production execution patterns on cloud data platforms.
- Ability to work effectively in a regulated or security-sensitive environment with strong expectations around access control, auditability, and operational discipline.
- Strong collaboration skills and comfort partnering with cloud/infrastructure, security, data engineering, and analytics stakeholders.
Preferred qualifications:
- Databricks certification and/or strong demonstrated expertise with Delta Lake, Unity Catalog, Workflows, and Databricks Asset Bundles.
- Experience with infrastructure-as-code and platform automation in enterprise environments.
- Experience supporting commercial and government or otherwise segregated environments with different compliance and access requirements.
- Experience in defense, aerospace, federal, or another regulated industry.
#LI-KE1
#LE
Full-time regular employee offer package:
Pay within range listed + Bonus + Benefits + Equity
Temporary employee offer package:
Pay within range listed above + temporary benefits package (applicable after 60 days of employment)
Salary compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. All offers are contingent on a cleared background and possible reference check. Military fellows and part-time employees are not eligible for benefits. Please speak to your talent acquisition representative for more information.
###
Shield AI is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please let us know.
Similar Jobs at Shield AI
Aerospace • Artificial Intelligence • Machine Learning • Robotics • Software
Develop state estimation and navigation algorithms for autonomous systems operating in GPS-denied environments. Build production-grade C++ software for embedded Linux robotic platforms, create modeling and simulation tools, and implement comprehensive testing and validation. Collaborate on roadmap planning and agile execution while improving benchmarking and performance-analysis pipelines. The role requires professional C++ expertise, state estimation algorithm experience, sensor-fusion familiarity, and the ability to deploy reliable autonomy software.
Top Skills:
Automated TestingBarometersC++CamerasCeresCi PipelinesEkfEmbedded LinuxGpsGraph-Based OptimizationGtsamImuLaser AltimetersMagnetometersMatlabOpenvinsParticle FiltersPythonSlamUkfVio
Aerospace • Artificial Intelligence • Machine Learning • Robotics • Software
Develop deep learning and 3D computer vision models for state estimation, visual navigation, localization, depth, correspondence, and pose estimation. Own data preparation, training, evaluation, benchmarking, and reproducible workflows. Integrate learned measurements into VIO and terrain-relative navigation systems, optimize models for onboard compute, and collaborate with deployment, software, systems, and flight-test teams to deliver production-ready Hivemind SDK components.
Top Skills:
3D Computer VisionDeep LearningDiffusion ModelsEmbedded Compute PlatformsFlow MatchingGpu ComputingModel QuantizationMulti-View GeometryOnnxPythonPyTorchSensor Data PipelinesSlamStructure From Motion (Sfm)TensorrtVisual-Inertial Odometry (Vio)
Aerospace • Artificial Intelligence • Machine Learning • Robotics • Software
Leads Shield AI’s Enterprise Data delivery organization, managing data engineering, analytics engineering, domain enablement, and governance teams. Translates enterprise data strategy into sequenced roadmaps, manages priorities, capacity, dependencies, risks, and stakeholder expectations, and ensures production-ready, governed data products. Partners with architecture, platform engineering, security, and business leaders on technical tradeoffs, data quality, lineage, reliability, and investment priorities. Also owns hiring, coaching, performance development, delivery metrics, and continuous improvement of the data operating model.
Top Skills:
DatabricksDatabricks SqlDatabricks WorkflowsDelta LakeLakehouse ArchitectureUnity Catalog
What you need to know about the Los Angeles Tech Scene
Los Angeles is a global leader in entertainment, so it’s no surprise that many of the biggest players in streaming, digital media and game development call the city home. But the city boasts plenty of non-entertainment innovation as well, with tech companies spanning verticals like AI, fintech, e-commerce and biotech. With major universities like Caltech, UCLA, USC and the nearby UC Irvine, the city has a steady supply of top-flight tech and engineering talent — not counting the graduates flocking to Los Angeles from across the world to enjoy its beaches, culture and year-round temperate climate.
Key Facts About Los Angeles Tech
- Number of Tech Workers: 375,800; 5.5% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Snap, Netflix, SpaceX, Disney, Google
- Key Industries: Artificial intelligence, adtech, media, software, game development
- Funding Landscape: $11.6 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Strong Ventures, Fifth Wall, Upfront Ventures, Mucker Capital, Kittyhawk Ventures
- Research Centers and Universities: California Institute of Technology, UCLA, University of Southern California, UC Irvine, Pepperdine, California Institute for Immunology and Immunotherapy, Center for Quantum Science and Engineering
