• Own the multi-region data architecture across six AWS-native engines: Aurora MySQL, Cassandra, ElastiCache (Redis / Valkey), DynamoDB, DocumentDB, and Neptune. For each, define the cross-region replication pattern (Aurora Global Database, DynamoDB Global Tables, DocumentDB Global Clusters, Cassandra multi-DC, MemoryDB or Valkey global datastore, Neptune Global Database) and the operational model that supports it. • Define and own the per-tier RTO and RPO commitments (Tier 0 / Tier 1 / Tier 2) and the cutover and failback runbooks and perform validation using Fault Injection Service / Game Days. • Sequence the data-layer cutover across all three program phases. Identify strong-consistency vs eventual-consistency decisions per workload, and where active-active is feasible vs where region-sharded ledgers or single-writer with replicated read-models is the right pattern. • Lead a team of Database Engineers across the engines. Set the technical bar, review designs, and unblock implementation. Partner with the client's DB and SRE leadership on architecture decisions and Operational Readiness Reviews. • Translate data-layer constraints into the application decomposition strategy. Where the monolith is being broken up into microservices, advise on data ownership boundaries (DDD-aligned), strangler-fig extraction patterns, CDC pipelines (DMS, DDB Streams, Kafka Connect), and the event-based contracts that decouple services from the monolith. • Produce the audit-grade artifacts the client and their regulators expect: replication runbooks, DR test evidence, RTO and RPO measurement methodology, and Well-Architected Reliability Pillar alignment.

Role Description The Senior Staff Data Engineer is responsible for the design and implementation of numerous complex data flows to integrate operational systems, external and internal satellites and deliver data for analytics, AI, business intelligence (BI) systems and applications(s). Individuals in this role will: - Design, build and maintain a scalable data infrastructure and implement integration pipelines for multiple systems upstream and downstream; - Identify integration patterns and design architecture based on business requirements and platform capabilities which may include defining patterns for data streaming; - Lead projects for data assets and mentor and coach engineers while collaborating with other teams and business for technical consult; - Champion data engineering across platforms; - Establish standards for platform(s) being used and share across teams and engineers. This role will be Residence-Based in the US. Qualifications - Minimum of a Bachelor’s degree in Computer Science, Software Engineering, or related field, and significant professional software engineering experience. Additional professional experience will be suitable in lieu of a Bachelor’s degree; - Professional data engineering experience to include: - Experience working with Relational Database technologies, such as BigQuery, Oracle, Teradata, Postgres and/or Redshift; - Demonstrated expertise engineering data intensive solutions using streaming and resource based design principles; - Hands on experience using programming languages such as Python, Java and/or Golang; - Relevant certifications as applicable; - Demonstrated experience with data architecture and modeling, including designing both logical and physical models; - Strong organizational, interpersonal, written and oral communication skills and desire to work in a highly collaborative environment; - Strong problem solving, analytical and proven project delivery skills; - Ability to work collaboratively in a team environment; - Focused on data accuracy, timely delivery and attention to detail; - Ability to quickly learn upcoming technologies and familiarity with the relevant industry trends; - Deep knowledge and demonstrated experience with Google BigQuery and Google Cloud Platform; - Experience with version control and related tools Github, GitLab and IaC (like Terraform or others). Requirements - One of the following: - Bachelor’s degree in Computer Science, Software Engineering, or related field, with 5+ years of related professional software engineering experience, OR - Master's degree with 3 years of relevant experience, OR - PhD with 1 year of relevant experience; - 9+ years of relevant experience is an acceptable substitute for degree requirement. - Professional data engineering experience to include: - 5+ years of experience working with Relational Database technologies, such as BigQuery, Oracle, Teradata, Postgres and/or Redshift; - 3+ years of experience engineering data intensive solutions using streaming and resource based design principles; - 5+ years of experience using programming languages such as Python, Java and/or Golang. Benefits - Employees can expect to be paid a salary between $123,760 - $185,640. - Additional compensation may include a bonus or commission (if relevant). - Other benefits include health care, vision, dental, retirement, PTO, sick leave, etc. - If selected for this role, the offer may vary based on market data/ranges, an applicant’s skills and prior relevant experience, certain degrees and certifications, and other relevant factors. Company Description Bayer is an Equal Opportunity Employer/Disabled/Veterans and is committed to providing access and reasonable accommodations in its application process for individuals with disabilities.

• Collaborate with product managers, designers, infrastructure teams, and other stakeholders to develop data management strategy. • Build and maintain data pipelines that clean, transform, and aggregate data from disparate sources. • Build and maintain Tableau dashboards and custom reports. • Create and maintain technical documentation, including system design, APIs, usage guidelines, and best practices. • Identify and address performance bottlenecks across the entire application. • Identify architecture, design, and operational improvements to existing applications.

• Collaborate with team members to define requirements and translate them into scalable data models and pipelines. • Develop and maintain ELT pipelines, ensuring data reliability and scalability for business reporting and analytics use cases. • Work with raw data and transform it into structured, usable formats for analytics and reporting. • Build and optimize SQL-based data models using dbt and other ETL tools. • Identify and implement improvements in data delivery, processing performance, and system efficiency. • Contribute to the team’s technical vision and bring innovative solutions to enhance data systems. • Partner with data engineering teams to drive standardization and consistency of product data modeling, schemas and insights reporting across the entire company.