Senior Technical Specialist

Job Summary

We are seeking two Senior/Expert Data Engineering and Data Architecture professionals to support strategic Finance Platform initiatives across Bayer Functions and Divisions. The ideal candidate combines deep technical expertise with strong implementation capabilities and is able to transform concepts, requirements, and architectural blueprints into reliable, production-ready solutions.

The individual is expected to work effectively within Bayer's existing architectural framework, collaborate closely with Lead Engineers and solution stakeholders, validate proposed approaches, and contribute hands-on to successful implementation and operationalization. This is not a purely advisory role - we are looking for engineers who can actively design, validate, build, and deliver.

Key Responsibilities

Solution Design, Validation & Implementation

• Participate in the design of enterprise-scale data solutions and platform capabilities.
• Challenge and validate technical concepts, assumptions, and implementation approaches.
• Translate architectural blueprints into practical and scalable implementations.
• Drive solution realization from initial concept through production deployment.
• Identify implementation risks, technical constraints, and optimization opportunities early in the delivery process.
• Ensure solutions are aligned with established architecture standards and engineering guardrails.
• Convert business requirements into maintainable, production-ready solutions.

Enterprise Data Engineering

• Design, develop, test, deploy, and operate enterprise-grade data solutions.
• Implement secure, scalable, and auditable data pipelines and data products.
• Build maintainable and reusable engineering assets.
• Optimize performance, quality, reliability, security, and cost efficiency.
• Ensure solutions can be successfully transitioned into long-term operations.
• Establish monitoring, operational controls, and support processes where required.
• Contribute to engineering standards and reusable implementation patterns.

Business & Solution Partnership

• Understand business processes and functional requirements.
• Collaborate with Product Owners, Architects, Engineers, Analysts, and Business Stakeholders.
• Evaluate solution alternatives and support technical decision making.
• Contribute to the definition of data assets, data services, and integration capabilities.
• Drive pragmatic solutioning while balancing business needs, architecture principles, and technical constraints.
• Communicate effectively across technical and non-technical audiences.

Multi-Team & Multi-Vendor Delivery

• Operate effectively within complex organizational environments.
• Collaborate with global delivery teams, internal Bayer organizations, and external service providers.
• Integrate seamlessly into existing team structures and ways of working.
• Work constructively within architectural guardrails and engineering standards provided by Bayer Lead Engineers.
• Contribute expertise while respecting established ownership, governance structures, and decision-making processes.
• Partner effectively with peers across Functions, Divisions, ED&A, Finance Platform teams, and external vendors.

DevOps & Operational Excellence

• Work within Agile and DevOps delivery models.
• Utilize Azure DevOps for planning, backlog management, sprint execution, and delivery coordination.
• Participate actively in implementation, testing, deployment, and operational handover activities.
• Support CI/CD, monitoring, troubleshooting, and continuous improvement.
• Ensure solutions are supportable, maintainable, and operationally sustainable.
• Contribute to release planning, deployment readiness, and production stabilization activities.

Skill Requirements

Databricks: Core delivery platform - workspace, clusters, notebooks, Workflows/Jobs, and SQL. Hands-on engineering experience mandatory.

Python: Primary language for pipeline development, data transformation, and automation. Production-grade experience required.

SQL: Data querying, transformation, and semantic layer work across relational and analytical platforms.

Microsoft Azure: Core cloud platform. Azure services used across data storage, processing, integration, and security layers.

DBT (Data Build Tool): Transformation layer - models, tests, documentation, and deployment within the data engineering workflow.

Git / GitHub: Source control, branching strategy, code review, and version management within team delivery.

Delta Lake & Unity Catalog: D elta Lake for ACID-compliant data management (schema evolution, time travel); Unity Catalog for governance, lineage, and access control.

Azure Data Factory & Azure Storage: ADF for data integration and orchestration; ADLS / Azure Blob for storage layer. Familiarity with Azure Key Vault for secrets management.

CI/CD & Infrastructure as Code: Pipeline automation (Azure DevOps Pipelines / GitHub Actions); IaC (Terraform / ARM) for repeatable, governed deployments.

Data Catalog & Metadata Management: Experience with data cataloguing tools and metadata management practices supporting data governance and discoverability.

Other Requirements

Required Functional Knowledge

• Enterprise Data Architecture - design principles, patterns, and guardrails.
• Data Engineering - pipeline design, build, deployment, and operations.
• Data Modelling & Dimensional Modelling - star schema, fact/dimension design.
• Lakehouse Architectures - medallion (bronze/silver/gold) design patterns.
• Data Integration - ingestion patterns, API integration, and data sourcing.
• Data Governance & Metadata Management.
• Data Quality Management - validation, profiling, and monitoring.
• Security and Privacy Controls - access management, encryption, audit.
• Audit and Compliance Requirements - especially in regulated Finance environments.
• Data Product Development and Production Operations.

Soft Skills & Personal Attributes

Communication & Collaboration

• Excellent spoken and written Business English.
• Ability to communicate effectively with technical and non-technical stakeholders.
• Strong presentation, workshop, and facilitation capabilities.
• Comfortable working in multinational, distributed, and multi-vendor teams.
• Able to build trusted working relationships across organizational boundaries.

Personal Attributes

• Analytical thinker - highly structured and organized.
• Delivery-oriented with strong hands-on implementation focus.
• Solution-oriented and pragmatic decision maker.
• Open to challenge and constructive feedback.
• Comfortable operating in complex and ambiguous environments.

Experience & Seniority Requirements

• Senior or Expert-level data engineering and/or data architecture expertise.
• Proven experience designing and implementing enterprise-scale data solutions.
• Strong track record delivering production-ready data pipelines and data products.
• Experience working within governed enterprise environments with audit and compliance requirements.
• Experience collaborating closely with cross-functional and multi-vendor delivery teams.
• Demonstrated success operating in multinational organizations.
• Experience supporting initiatives from concept through productive operation.
• Ability to rapidly integrate into existing teams and contribute value with minimal ramp-up.