Data Architect

We are seeking an experienced Data Architect with strong expertise in information architecture and data modeling to serve as the data design authority within Osol's Data & Analytics practice. In this role, you will shape how data is structured, governed, and trusted across client consulting engagements, industrial data platforms, and the Industrial AI initiatives implemented in our manufacturing plants. The primary focus is on consulting services, and the ideal candidate will have extensive experience modeling complex enterprise data end-to-end, preferably within finance and manufacturing sectors. You should be comfortable engaging with clients and senior executives, efficiently extracting critical information from busy subject-matter experts during concise, well-prepared sessions.

Key Responsibilities:
- Service offerings: Define the scope, approach, deliverables, team structure, and estimates for Osol’s Data & Analytics services, including Data Strategy & Advisory, Data Engineering & Platform Modernization, Data Governance, Quality & Lineage, Business Intelligence & Analytics, and DataOps.
- Reusable assets: Develop assessment frameworks, reference data architectures, industry-specific data model templates (with an initial focus on finance and manufacturing), and governance operating model templates.
- Presales: Lead discovery workshops, shape data solutions, author data architecture sections for proposals and RFP responses, estimate effort, and present to client executives alongside technology partners. Contribute to case studies and thought leadership initiatives.
- Delivery leadership: Act as lead data architect and design authority on engagements, guiding data engineers and BI developers while ensuring the quality of data designs and deliverables.
- AI-assisted development: Utilize AI coding tools such as Claude Code to generate and review code, SQL, and data models for complex use cases across multiple programming languages.
- Use-case data readiness: Evaluate the availability, quality, history, and accessibility of plant data to prioritize Industrial AI use cases like predictive maintenance.
- Source discovery: Collaborate with plant experts in historians, control, maintenance, ERP, laboratory systems, and operator logbooks to document data sources and draft acquisition specifications that comply with read-only access and plant security policies.
- Industrial information models: Design reusable, configuration-driven plant information models covering assets, functional locations, events, materials, and streams aligned with ISA-95 and ISO 14224 standards. Ensure tag-to-asset mapping and AI-ready datasets to promote reuse across use cases, plants, and clients.
- Platform data design: Review industrial data platform scopes, requirements, and designs throughout their lifecycle from a data perspective. Advise architects on metadata, data quality, lineage, master and reference data, ownership, client data separation, and retention policies.
- RFI / RFP evaluation: Assess vendor responses for enterprise data, analytics, and AI platforms from a data architecture standpoint. Help define evaluation criteria and scoring, participate in vendor demonstrations and proofs of concept, and draft data requirements for RFP stages.

Required Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Information Systems, Software Engineering, Engineering, or a related field.
- Over 12 years of experience in data management, data warehousing, or data engineering, including at least 5 years in data architecture or data modeling roles.
- Proven track record as lead data architect or modeler on a minimum of two enterprise data platform or data warehouse programs that reached production, ideally within consulting firms or technology vendors. Experience in finance and/or manufacturing is strongly preferred.
- Strong expertise in enterprise data warehouse, data lake, and lakehouse platforms deployed in cloud, on-premises, or hybrid environments.
- Advanced skills in data modeling techniques including dimensional (Kimball), normalized (3NF), and Data Vault 2.0 methodologies.
- Deep knowledge of information architecture components such as business glossaries, taxonomies, ontologies, canonical and semantic models, metadata, master and reference data, and data domain ownership.
- Familiarity with data governance practices aligned with DAMA-DMBOK, including data quality, lineage, classification, privacy, retention, and stewardship.
- Proficiency in advanced SQL for data profiling and analysis, along with working knowledge of ELT, change data capture, streaming, and batch processing patterns to guide engineering teams.
- Domain expertise in finance (general ledger, chart of accounts, cost and profitability, management reporting) and manufacturing (plant historians, asset hierarchies, maintenance, time-series data), including SAP or Oracle ERP structures.

Preferred Qualifications and Benefits:
- Certifications such as DAMA CDMP, TOGAF, or certifications from major data platforms like Microsoft, Databricks, or Snowflake are advantageous.

This role offers the opportunity to lead impactful data architecture initiatives within a dynamic industrial environment, working closely with clients and technology partners to deliver innovative, AI-enabled data solutions.