Monsanto is passionate about using science and technology to improve agriculture. Monsanto scientists are conducting research and development (R&D) to revolutionize plant breeding and biotechnology. The Technology Pipeline Solutions (TPS) team within IT works directly with the Monsanto scientists to develop software platforms that enable these research and development efforts. The accepted candidate will leverage their data architecture expertise to play a key role in defining and delivering breakthrough science in high throughput R&D platforms for Monsanto.
Be the subject matter expert for assigned data domain/subject area (e.g. Product, Customer, Location, etc)
Define relationships and touch points between data domains
Cross train on at least one other domain area within Data Architecture team
Lead requirements gathering sessions to build canonical model for assigned subject area
Requirements mapping and gap analysis
Analyze complex data structures to build a common data model
Mine existing systems to derive source to target to canonical models
Assist with data integration during M&A and divestitures
Establish policy for building semantic transformation specifications
Composition of accurate and complete definitions for entities and attributes
Work with business and workflow analysts to understand and define subject area data usage patterns and desired business functionality.
Work with build teams to socialize concepts in the canonical model, to ensure user stories move toward implementation of the vision, revising the subject area as needed.
Ensure quality metadata is captured
The candidate must be capable of understanding data strategy at the enterprise level, implement the data architecture on the project level, and have the excellent communication skills necessary to successfully interact with solution architects, product owners, project managers, developers, business analysts, and testers.
Investigate source systems to locate and understand existing data domain areas and integration points across the research pipeline
Develop integration designs consistent with Information Management principles, collaborating with key IT stakeholders to ensure that solutions meet project requirements and fit within the broader enterprise architecture context.
Provide thought leadership in data management strategies, governance, and design.
Undergraduate degree in applicable area of expertise or equivalent work experience
+3 years of Enterprise Data Architect experience
+5 years of data modeling experience
Experience in building complex, large scale logical data models with PowerDesigner, ERwin or E/R Studio
Experience with SQL and NoSQL database technologies (e.g. Oracle RAC, PureData, BigTable, Hadoop/HBase, Splunk, Neo4j, Cassandra, etc.) and enterprise Information Integration
Experience with Agile Data Modeling concepts
Understanding of master data concepts (e.g., System Of Record), data governance principles
Must have a proven track record of working independently and collaboratively
Must be able to communicate very well, both verbally and in writing
Experience describing abstract concepts, designs and relationships within a data domain as it applies to many applications and products.
Engage with project teams to ensure their solutions are consistent with the defined future state enterprise architecture.
Able to communicate clearly with peers as well as with management, and provide technical leadership to more junior team members.
Flexibility to adjust to changing requirements, schedules, and priorities.
The ability to socialize ideas, make recommendations, and gather team consensus to move forward.
Ability to perform in a loosely-structured environment identifies opportunities for improvement, proactively solicit stakeholder input, and drive development of a solution.
Experience in formal delivery methodologies and platforms as they relate to enterprise data architecture, including hands on technology experience
Experience with public Ontologies and Taxonomies (e.g. GO, SO, PATO, GEO, TO, KEGG, etc)
Active member in DAMA or other professional groups
Experience in data quality and data integration
Experience with large scale data remediation projects
Experience with Life Sciences industry