Staff Software Engineer

Bayer•Tulsa, OK

8d•Remote

About The Position

At Bayer, we are driven by the vision of solving the world's toughest challenges and striving for a world where 'Health for all Hunger for none' is a reality. We foster a culture of energy, curiosity, and dedication, learning from diverse perspectives to expand our capabilities and redefine the impossible. The Digital Farming Solutions Engineering team builds platforms that empower farmers to make smarter, high-stakes decisions every season. As a Staff Engineer, you will ensure these platforms are architected for scale, reliability, and the demands of AI and machine learning globally. You will set technical direction, guide teams on complex problems across precision agriculture data, geospatial imagery, and cloud infrastructure, and resolve systemic challenges. A key part of your role will be to elevate the engineering culture by mentoring senior engineers, championing best practices, and ensuring the maintainability and extensibility of our software.

Requirements

BS or equivalent in Computer Science or related technical field or equivalent combination of education and experience
8+ years of hands-on experience in software development
Proven history of ability to quickly learn new skills and technologies
Experience leading across team(s) the development of highly scalable cloud based (AWS preferred) microservices and data pipelines using container technologies (e.g., Docker)
Ability to take ambiguous requirements in complex domains and distill them down into actionable tasks for other engineers
Excellent written and verbal communication skills including the ability to present complex information in a clear and concise manner to engineering and non-engineering audiences
Expert knowledge in at least one modern backend programming language (e.g., Java, Python) with the ability to write understandable, testable code with an eye towards maintainability.
Strong understanding of at least one containerization technology (e.g., Docker)
Strong understanding of at least one public cloud platform.
Strong knowledge of software development methodologies and best practices.
Experience using observability techniques to drive support and improvements of systems
Proficiency with managing CICD pipelines (e.g., GitLab Pipelines, GitHub Actions)

Nice To Haves

Experienced with modern REST and GraphQL (Strong Plus) API design
Exceptional customer focus, ownership, and drive.
Proven ability to develop other strong senior engineers into Staff engineers, operating and leading their own initiatives with org-wide scope and high impact.
Experience with relational and non-relational data stores. Expert knowledge of at least one. Strong knowledge and experience with SQL including performance tuning and query optimization.
Expert knowledge of at least one containerization technology (e.g., Docker)
Strong understanding of multiple AWS capabilities (EC2, S3, RDS, SQS, etc.). Expert knowledge of multiple AWS capabilities.
Strong experience with event driven architectures using messaging middleware (SQS, JMS, etc.)
Experience with layered geospatial data structures and data representations (Strong Plus)

Responsibilities

Define and drive the technical strategy for the organization's highest-priority initiatives, from architecture through delivery.
Bring clarity to ambiguous, high-complexity problems — translating strategic goals into actionable technical direction that teams can execute with confidence.
Set and evolve the engineering standards for reliability, observability, and code quality that define how Digital Farming Solutions builds software at scale.
Actively develop senior engineers into Staff-level leaders, sponsoring their growth into initiatives with organization-wide scope and impact.
Champion a culture where engineering decisions are grounded in farmer outcomes — connecting technical tradeoffs directly to the real-world impact on the people we serve.
Shape the technical approach for the full spectrum of agricultural data — from on-farm precision sensors to satellite and geospatial imagery — ensuring platforms handle it with accuracy, scale, and performance.
Drive architectural decisions for data-intensive, algorithmically complex precision agriculture systems, partnering with data science and product to turn research into production-grade capabilities.