Senior Manager, Cloud Architect

Lincoln Institute of Land Policy•Cambridge, MA

1d•$95,000 - $115,000•Remote

About The Position

The Center for Geospatial Solutions (CGS) is seeking a Senior Manager, Cloud Architecture to serve as the lead backend and cloud architect for a complex, multi-year federal program, in addition to working on a second program focused on water reservoir system analysis and data interoperability. Reporting to the Associate Director of Data Engineering, the Senior Manager, Cloud Architecture will serve as the technical and managerial lead for backend development on a federal program, dedicating most of their time to this work. Their remaining time will aid other CGS projects, where this role will contribute in shaping technical strategy and system design across programs. Under the guidance of Data Engineering and Science leadership, they will define scalable backend architecture, shared cloud services, and production systems that support data ingestion, metadata and discovery, large-scale geospatial processing, model execution, quality assurance and validation, and deliver data and services to users and partners, translating program requirements into technical strategy and standards that can extend beyond a single program. This is a hands-on engineering role that combines backend software development, cloud architecture, platform engineering, DevOps, security, and cost optimization. This role will oversee a small team of backend developers, providing technical direction, empowerment, and mentorship to the team. The successful candidate will work closely with data scientists, AI engineers, GIS professionals, project managers, and external partners to turn complex technical workflows into maintainable, observable, reproducible, and well-governed systems.

Requirements

Undergraduate degree or equivalent professional experience in computer science, software engineering, information systems, engineering, geography, or a related technical field.
5+ years of experience in backend software engineering, cloud engineering, platform engineering, DevOps, site reliability engineering, or a closely related role.
5+ years of experience solving geospatial cloud architecture problems.
2+ years of experience successfully empowering and developing staff.
Demonstrated experience designing and building production backend services, APIs, data platforms, or distributed systems using Python or another modern programming language.
Strong hands-on experience designing, deploying, and operating production workloads in AWS, GCP, or other cloud platforms.
Experience architecting data-intensive or compute-intensive systems that must process large volumes of data reliably and efficiently.
Demonstrated experience with AWS services such as S3, Batch, ECS or EKS, Lambda, Step Functions, RDS or Aurora, API Gateway, IAM, and CloudWatch.
Experience with relational databases and SQL, including data modeling, query optimization, and application integration; PostgreSQL or PostGIS experience is particularly relevant.
Working knowledge of Esri enterprise GIS architectures, with the ability to support licensing decisions, enterprise deployments, and alignment of Esri products and user access with organizational needs.
Working knowledge of geospatial data standards and architecture (e.g., OGC standards) and how they inform system design.
Experience with infrastructure as code, CI/CD, containers, Linux, automated testing, deployment automation, and production monitoring.
Working knowledge of cloud networking, identity and access management, secrets, storage, compute, databases, queues, APIs, and observability.
Demonstrated experience analyzing and optimizing cloud performance and cost, including the ability to explain cost drivers and recommend practical improvements.
Experience with Git/GitHub and collaborative software-development practices, including code review and technical documentation.
Strong troubleshooting, systems-thinking, and communication skills, including the ability to explain architecture, risks, and tradeoffs clearly.
Ability to work effectively with multidisciplinary teams and external partners in a fast-moving, fully remote environment.
U.S. Citizen, or legally authorized to work in the U.S. with no need for future sponsorship.

Nice To Haves

Experience working with geospatial APIs and data interoperability standards such as pygeoapi
Experience building backend systems for Earth observation, environmental modeling, raster processing, scientific computing, or other large-scale analytical workflows.
Experience with workflow and data orchestration tools such as Airflow, Prefect, Dagster, Argo, or comparable technologies.
Experience with MLOps, model registries, experiment tracking, GPU workloads, distributed training, or scalable inference.
Experience supporting government or federally funded programs and associated security, documentation, reporting, data-handling, or compliance requirements.
Experience with metadata catalogs, data discovery systems, provenance, persistent identifiers, or interoperable data services.
AWS certification or demonstrated experience with FinOps, disaster recovery, production incident management, or security architecture.
Experience supporting government, nonprofit, consulting, or mission-driven technical projects.

Responsibilities

Responsible for backend and cloud engineering work on a federal program, coordinating closely with technical leads, project manager, partner organizations, and federal stakeholders.
Translate program milestones, analytical workflows, user needs, performance requirements, and partner interfaces into technical architecture, implementation plans, and estimates, documenting decisions and progress where needed.
Identify architectural dependencies, performance constraints, delivery risks, and technical debt early; recommend practical options and tradeoffs to project and technical leadership.
Design modular backend architecture that connects data ingestion, cataloging, geospatial processing, machine learning, quality assurance, metadata and discovery, and data or service delivery.
Define service boundaries, APIs, data schemas, workflow patterns, and integration contracts that allow components developed by multiple teams and partners to work together reliably.
Build shared platform components and integration layers that reduce duplicative, one-off pipelines and aid reuse across regions, analytical methods, and future CGS projects.
Design and implement secure, scalable cloud architectures for large geospatial datasets, distributed processing, machine learning, databases, APIs, and backend services.
Select appropriate combinations of containers, batch computing, serverless services, workflow orchestration, managed databases, and other cloud capabilities based on workload requirements.
Contribute technical estimates, architecture narratives, implementation plans, progress reporting, and documentation required for a complex federally funded program.
Directly supervise and empower backend data development staff, overseeing their day-to-day work, performance and professional growth.
Delegate and manage backend implementation work across team to ensure work is scoped appropriately and aligned with overall envisioned architecture.
Work with leadership to establish cloud and backend engineering practices that are reproducible across projects.
Communicate technical options, risks, cost implications, and tradeoffs clearly to both technical and nontechnical colleagues.
Design and oversee the implementation of production-quality backend services, APIs, data-access layers, workflow services, and reusable software components using Python or another appropriate language.
Build services that enhance functions such as data submission and ingestion, metadata management, job scheduling and status, geospatial processing, model outputs, validation status, search and discovery, and data delivery.
Work with relational and spatial databases, object storage, queues and event systems, caching, API gateways, authentication and authorization, and other backend infrastructure.
Evaluate when to build, adapt open-source technologies, or use managed services based on mission needs, delivery timelines, security, maintainability, and total cost.
Provision and manage compute, object storage, databases, networking, identity and access management, secrets, logging, and related cloud services using infrastructure as code.
Establish clear tagging, budgeting, cost allocation, and monitoring practices so cloud spending can be understood by project, environment, workload, and program activity.
Continuously optimize cloud cost and performance through rightsizing, autoscaling, storage lifecycle policies, scheduling, caching, serverless or spot capacity, and other practical FinOps approaches.
Support separate development, testing, staging, and production environments while maintaining reproducibility and minimizing unnecessary infrastructure expense.
Design and maintain CI/CD pipelines for infrastructure, backend applications, data workflows, and analytical services.
Containerize services and workflows using Docker and support orchestration through managed container platforms, Kubernetes, batch systems, or serverless services as appropriate.
Implement logging, metrics, tracing, monitoring, alerting, backups, recovery procedures, and operational runbooks.
Implement security controls (least-privilege access, encryption, secrets management) in alignment with CGS and Lincoln Institute security policy.
Support the technical documentation, access controls, change records, data-handling practices, and other evidence needed for federal, partner, client, and Lincoln Institute requirements.
Troubleshoot backend, performance, reliability, networking, permissions, data movement, and deployment issues across development and production environments.

Benefits

3x employer contribution towards retirement matching your employee contribution up to 15%
health insurance
dental insurance
vision insurance
100% reimbursement of the health care deductible through a health reimbursement account
short-term disability coverage
long term disability coverage
paid parental leave
voluntary insurances such as accident insurance
health care flexible spending
dependent care flexible spending
paid time off for holidays, vacation, personal, sick, bereavement, and jury duty
office closure between December 24 – Jan 1 each calendar year
flexible schedule and option for a compressed 4 day workweek
tuition and staff development reimbursement
pet insurance
Employee Assistance Program