Title: Data Architect (Semantic and Graph Technologies)
Location: Hybrid in Atlanta or Alpharetta, GA (2 days WFH)
Industry: Financial Services
Compensation: $60-$82/Hour (Contract-to-Hire)
We’re partnering with our client to find a Data Architect (Semantic and Graph Technologies). The Data Architect (Semantic and Graph Technologies) will play a critical role in shaping the enterprise data architecture by leveraging taxonomy development, ontology management, graph schema design, and semantic AI techniques. This individual will be responsible for designing and implementing data models that support business intelligence, AI-driven analytics, and data governance initiatives.
As part of the Global Data Office, the role will report to the Head of Enterprise Data Architecture and act as a subject matter expert in aligning data structures with enterprise data strategy. The Data Architect (Semantic and Graph Technologies) will collaborate with IT, business, and analytics teams to develop scalable and compliant data solutions that drive innovation and efficiency.
The ideal candidate will have a strong technical background in graph databases, AI/ML, NLP, cloud platforms, and data governance frameworks while possessing excellent collaboration and communication skills to translate complex data structures into business-aligned solutions. This role is pivotal in enabling the organization's digital transformation and enhancing its ability to leverage AI-powered insights and advanced data management practices.
Key Responsibilities
- Taxonomy Development and Management:
- Design and build domain specific taxonomies and ontologies.
- Maintain and update taxonomies as business needs evolve.
- Utilize taxonomies to improve search, information retrieval, and knowledge management.
- Graph Schema Design and Implementation:
- Develop and implement graph data models that accurately represent the complex relationships between entities in the financial domain.
- Work with database administrators and developers to ensure optimal performance and scalability of the graph database.
- Semantic AI Integration:
- Leverage AI technologies such as natural language processing (NLP) and machine learning (ML) to enhance the capabilities of taxonomies and graph data models.
- Develop and implement algorithms for semantic search, entity recognition, and relationship extraction.
- Support the design and implementation of data models that enable the acquisition, production, storage, access, analysis, and delivery of data to meet business objectives, acting as a bridge between the data requirements of business and analytic processes, and the physical implementation of that data in technology infrastructure.
- Align components of the data environment with the enterprise data strategy – by understanding data structures and flows and how they relate to business use.
- Work with IT to align underlying physical sources with the specified data architecture.
- Support management of the data architecture through various governance processes.
- Support the design and evolution of the architecture of our data asset to drive value and NPIs.
Skills & Qualifications
- Familiarity with cloud and on prem technologies, structured and unstructured data, streaming and reposed data.
- Experience with data modeling techniques and tools.
- Experience in analytics and AI solutions such as NLP and ML.
- Strong understanding of semantic web technologies, taxonomies, ontologies, and graph databases.
- Knowledge of Python programming language.
- Experience with a data visualization and reporting tool.
- Experience working closely with business and analytics teams to understand their needs (e.g., design thinking sessions, business requirements, agile methods, etc.), maintain ongoing engagement through development, rollout, and improvement cycles.
- Hands on Technical capabilities such as - designing and developing relational and non-relational data models.
- Excellent communication and collaboration skills, with proven ability to work independently or as part of a team with strong attention to detail.
Education & Experience
- BS degree in a STEM major or equivalent discipline; Master’s Degree strongly preferred.
- 5 - 7 years of data analysis and modeling experience, particularly applied to common and specific business uses.
- Cloud and other relevant technical certifications strongly preferred.
What could set you apart
- Technical Advising / Consulting - Experience modeling entities, relationships, attributes, and abstract data building blocks for a given business case, considering both specific database / physical design as well as the logical and conceptual design required for business use.
- Domain Knowledge: A solid understanding of financial products, markets, and regulations is crucial for effective communication and collaboration with business stakeholders.
- Data Security and Privacy: The financial services industry is subject to strict data security and privacy regulations.
- Risk Management: Understanding of risk management principles and how data can be used to identify and mitigate risks is important in the financial sector.
