Senior Staff Data Engineer

At Bayer we’re visionaries, driven to solve the world’s toughest challenges and striving for a world where 'Health for all Hunger for none’ is no longer a dream, but a real possibility. We’re doing it with energy, curiosity and sheer dedication, always learning from unique perspectives of those around us, expanding our thinking, growing our capabilities and redefining ‘impossible’. There are so many reasons to join us. If you’re hungry to build a varied and meaningful career in a community of brilliant and diverse minds to make a real difference, there’s only one choice.

Senior Staff Data Engineer 

PURPOSE:

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.

KEY TASKS AND RESPONSIBILITIES: 

• Data Integration Design:
• Design and implement Market360 Product Supply and Commercial data models using various GCP technologies;
• Develop solutions/ETL that ingest data from multiple sources and deliver solutions for BI Reporting and advanced analytical capabilities;
• Establish standards, keep them up to date and ensure adherence to them
  Keep abreast of best practice in industry and across platforms;
• Data Modeling:
• Design data models that follow data warehousing industry standards and maintain required documentation or reverse engineer existing models as needed;
• Work across platforms, product teams, and customer teams, recognizing opportunities for the reuse and alignment of data models in different organizations;
• Data Analysis and Synthesis:
• Exploratory data analysis, data profiling, data cleaning & processing - Perform data discoveries to understand data formats, source systems, etc. and engage with business partners in this discovery process;
• Assemble and evaluate data such that new insights, solutions, and visualizations can be derived;
• Bring multiple data sources together in a conformed model for analysis;
• Data Development Process:
• Ensure data solutions are scalable, repeatable, optimized and follow governance and engineering guidelines;
• Assess technical requirements to deliver streaming/real time or batch solutions as needed;
• Assess data delivery/access patterns to deliver data as API, Kafka or data marts;
• Establish enterprise-scale data integration procedures across the data development life cycle and ensure that teams adhere to them;
• Challenge and manage team to improve processes and methodologies to deliver cost optimized solutions in a timely manner;
• Programming and Build:
• Use agreed standards and tools to design, code, test, correct and document moderate-to-complex programs and scripts from agreed specifications and subsequent iterations;
• Collaborate with others to review specifications where appropriate;
• Metadata Management:
• Develop and implement solutions, metadata and documentation that support AI capabilities;
• Design an appropriate metadata repository and present changes to existing metadata repositories;
• Understand a range of tools for storing and working with metadata.
  Provide oversight and advice to more inexperienced members of the team;
• Communicating Between Technical and Non-Technical Colleagues:
• Lead and participate in design sessions with Data Stewards, Engineering leads, Data Scientists, Product Managers, business and IT stakeholders, that result in design documentation for data processing, storage and delivery solutions;
• Manage active and reactive communication;
• Support or host diff