Position: Data Architect
Term: 6-months, contract-to-hire
Location: Remote, USA
Approved states for hiring: Alaska, Arizona, Delaware, Florida, Hawaii, Idaho, Illinois, Indiana, Michigan, Minnesota, Mississippi, Missouri, Montana, Nebraska, New Hampshire, North Carolina, North Dakota, Oklahoma, Ohio, South Carolina, South Dakota, Tennessee, Texas, Utah, Vermont, Virginia, West Virginia, Wisconsin, Wyoming
The Data Architect is primary responsible to design, deploy, manage and mature client’s data architecture to enable the intended business architecture. Key functions include: understand business strategy and architecture, set data architecture principles, create data models to describe key data assets and their business value, develop roadmap for data and data technologies to enable implementation of intended business architecture and apply architecture principles and standards to broadly influence choice, timing, pace and maturation of various information technology platforms.
As an integral part of Enterprise Architecture, this role is expected to foster effective relationships in Business and IT, contribute to the development of overall architecture practice and strategy, and support enterprise initiatives that promote data dexterity and stewardship at the client.
- Develop target data architecture that enables intended business architecture and architecture vision, while addressing request for architecture work and stakeholder concerns.
- Identify candidate architecture roadmap components based upon gaps between baseline and target data architectures.
- Create data and information architecture maps, viewpoints and metadata to contribute to business capability framework for domain and enterprise models.
- Create and maintain data maps of internal and external sources of data, including descriptive business meaning of associated data, information about each source’s provenance from origination(s) to target(s), its use, timeliness, associated quality metrics, and details about applications or organizations that maintain data and the technologies in which it is stored, integrated, manipulated, analyzed, and consumed.
- Where possible, data documentation should include associated semantics including mapping to biomedical standards to adequately understand subtle differences in meaning and promote the reuse, interoperability, and effective analysis of data.
- Define integrative views of data that draws from across the enterprise to meet Business needs.
- Work with Business, business architects, data technology engineers, and application designers to identify and model integrative data views and determine service requirements attributes, including relative data quality, currency, availability, response times and data volumes with associated technology solutions and processes (from procurement to use/consumption).
- Coordinate work across IT teams and business partners (including stakeholders in the business where appropriate), to document data throughout the virtual enterprise including documentation of source and destination of each step, data currency and associated processes (e.g., data transformation aggregation, calculation or analysis, storage, consumption/use).
- Partner with IT functions and various Business areas to provide options and recommendations and assist in brokering internal agreements regarding use and sharing of data (including associated access controls for sensitive data) across systems and groups.
- Define and establish metadata standards and processes including business descriptions of the data, details of any calculations or summaries, descriptions of data sources, indications of quality and accuracy, and applicable biomedical standards and taxonomies.
- Define shared canonical view of data across data sources to centralize data transformations and data reuse across the enterprise.
- Define technical standards and guidelines for data technologies. Examples include processes and models of selected entities, objects and processes for architected data stores, technologies for extraction-transform-load processes, interfacing, Web-services, and data orchestration, analysis (including big data applications and high-performance computing).
- Establish process standards to promote reuse and interoperability of existing data stores, applications, and platforms.
- As part of Enterprise Architecture, investigate emerging technologies and new releases to ensure standards are up to date, participate in proof-of-concept and other projects.
- Establish and define analytics solution and data technologies system standards (including solution tiers), and supporting technology processes for information assets (e.g., reports, dashboards, analyses).
- Evaluate the impact of projects on enterprise and data architecture and recommend solutions that align with target state architecture.
- Design cost effective, secure and reliable data technologies and analytic solutions that align with enterprise architecture.
- Identify, document and promulgate interoperability, reuse and mechanisms for harvesting additional data value with technologies and solutions.
- Provide templates and guides for applications teams in the process of application build and configuration to document and maintain associated metadata about application data and its dependencies.
- Establish process standards for application and business teams, if applicable, with decision-rights over application configuration and data processes to maintain documentation throughout the lifecycle of an application, particularly as part of enhancements, upgrades, or other changes.
- Develop and execute a communications program to articulate the benefit of Enterprise Architecture overall and data architecture in particular across the enterprise.
- Establish appropriate forums in collaboration with Data Technologies and Analytics where appropriate for stakeholders to communicate requirements and priorities, provide input and discuss decisions.
- Support data stewards who educate the Business on the importance of data quality thought its entire lifecycle, principles and standards for creating data of high value and quality, data semantics and opportunities to improve data quality.