Python)

Bayer-posted about 2 months ago

$136,930 - $205,394/Yr

Full-time • Mid Level

Remote • Tulsa, OK

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As a Senior Staff Data Engineer, you will play a pivotal role in shaping and coordinating technical strategies across key business organizations. You will leverage your mastery in both technical and domain areas to collaborate with Staff Engineers, Engineering Managers, and senior leaders in setting long-term strategic goals. Your innovative solutions will continue to build on Bayer Crop Science's market advantage, ensuring our technical strategies support global business leads effectively.

Lead and participate in design sessions with enterprise and hub data stewards, engineering teams, data scientists, product managers, business, and IT stakeholders, resulting in documentation for data processing, storage, and delivery solutions;
Understand business capability needs and processes as they relate to IT solutions through partnering with Product Managers and business and functional IT stakeholders, applying this knowledge to influence business goals;
Initiate and lead evaluations of new technologies, such as Domino or Redshift, or new languages like Go or React, including performing POCs and presenting results to provide technical recommendations;
Help the team establish and improve processes and methodologies, such as SCRUM or Kanban, and/or lead piloting new ones;
Implement data solutions according to design documentation using a variety of tools and programming languages, like Kafka, SQL, and non-SQL databases, Scala, Go, etc., following the team's established processes and methodologies;
Facilitate and participate in code reviews, retrospectives, functional and integration testing, and other team activities focused on improving the quality of delivery;
Provide reliable estimates for large-scale projects;
Lead collaboration with business partners, product owners, other engineers, and data stewards within the team and across data, analytics, and product teams on planning and aligning roadmaps, delivery dates, and integration efforts;
Coach and mentor junior and aspiring data engineers on the team and across the data and engineering communities;
Present on various technical topics to internal and external audiences, for example, at national and international technical conferences;
Facilitate various cross-team and department efforts, like scrum of scrums and release planning, focused on large-scale roadmap alignments, sharing information, solving a broad variety of problems, or improving processes;
Discuss work, provide details, and influence the right level of audience such as senior leadership, business partners, data scientists, and engineering teams;
Create and maintain design and code documentation in GitHub, Haystack, SharePoint, and/or other repositories used by the team.

Must have one of the following: High School diploma with at least 14 years of relevant professional software engineering experience; OR Bachelor's degree in Computer Science, Software Engineering, or related field and at least 10 years of relevant professional software engineering experience; OR Master's degree and at least 7 years of relevant professional software engineering experience; OR Ph.D. with at least 5 years of relevant professional software engineering experience;
Significant professional experience with and deep knowledge of: Authoring high-quality Golang, Go, and or Python code to deliver new features, evolve existing services, and resolve defects;
API design principles (REST, gRPC) and API gateway technologies;
Google Kubernetes Engine and Google Cloud Platform;
Key architectural patterns, such as microservices and event-driven architecture, with a deep understanding of their related trade-offs;
Best practices for Continuous Integration and Continuous Deployment (CI/CD);
Infrastructure as Code (IaC) tooling (e.g. Terraform, etc.);
Deep familiarity with: Data architecture and modeling concepts and paradigms;
Observability concepts, such as Service Level Objectives (SLOs) and Service Level Indicators (SLIs), and related tooling (e.g., Datadog, OpenTelemetry, etc.);
Being a productive reviewer of code, with an ability to coach others on the differentiation of style and correctness;
Publish/subscribe models and supporting technologies (e.g., Apache Kafka, Google Pubsub, AWS SNS/SQS, etc.);
Some professional experience with: Relational databases and declarative query languages such as SQL (e.g., Oracle, Postgres, etc.);
Git version control system and related tools such as GitHub or GitLab;
Designing, building and running large-scale systems.

At least 5 years of professional experience modeling large datasets;
At least 6 years of experience engineering data-intensive software using streaming and resource-based design principles;
At least 4 years of experience in at least one NoSQL database, such as Neo4j, Cassandra, etc.;
At least 7 years of demonstrated experience with programming languages such as Python or Go;
Demonstrated experience migrating Python codebases to Go;
Strong understanding of cloud-native security best practices;
Strong understanding of DevOps best practices;
Direct experience implementing service meshes (e.g., Istio) in Kubernetes environments;
Strong understanding of Kubernetes architecture and concepts with direct experience deploying software using the Kubernetes control plane;
A demonstrated understanding of Machine Learning and Generative AI concepts;
Knowledge of the agricultural domain.

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