Data Architect

Job Description

We are looking for a Data Architect who is equally strong in information architecture and data modelling to be the data design authority for Osol's Data & Analytics practice. You will shape how data is structured, governed and trusted across client consulting engagements, industrial data platforms and the Industrial AI program we are delivering in our group's manufacturing plants.Consulting services are the primary focus. The ideal candidate has modelled complex enterprise data end to end preferably in finance and manufacturing is at ease with clients and senior executives, and gets what they need from busy subject-matter experts in short, well-prepared sessions.

Key Responsibilities

Service offerings: Detail the scope, approach, deliverables, team shape and estimates of Osol's Data & Analytics services Data Strategy & Advisory, Data Engineering & Platform Modernization, Data Governance, Quality & Lineage, Business Intelligence & Analytics, and DataOps.Reusable assets: Build assessment frameworks, reference data architectures, industry data model templates (finance and manufacturing first) and governance operating model templates.Presales: Lead discovery workshops, shape data solutions, write the data architecture sections of proposals and RFP responses, estimate effort, and present to client executives with our technology partners; contribute to case studies and thought leadership.Delivery leadership: Lead engagements as lead data architect and design authority, directing data engineers and BI developers and owning the quality of data designs and deliverables.AI-assisted development: Use Claude Code or similar AI coding tools to generate and review code, SQL and data models for complex use cases across multiple languages.Use-case data readiness: Assess the availability, quality, history and access of plant data to prioritise Industrial AI use cases such as predictive maintenance.Source discovery: Document plant data sources with plant experts historians, control, maintenance, ERP and laboratory systems, and operator logbooks and write acquisition specifications that respect read-only access and plant security rules.Industrial information models: Design reusable, configuration-driven plant information models assets, functional locations, events, materials and streams aligned with ISA-95 and ISO 14224, with tag-to-asset mapping and AI-ready datasets, so each new use case, plant and client reuses what the last one built.Platform data design: Review the scope, requirements and designs of industrial data platforms from a data perspective throughout their life cycle, and advise architects on metadata, data quality, lineage, master and reference data, ownership, client data separation and retention.RFI / RFP evaluation: Review vendor responses for enterprise data, analytics and AI platforms from a data architecture perspective, help define evaluation criteria and scoring, assess vendor demonstrations and proofs of concept, and draft data requirements for the RFP stage.Key Skills:Data architecture: Proven architect of enterprise data warehouse, data lake and lakehouse platforms delivered to production in cloud, on-premises or hybrid environments.Platform depth: Deep, production-grade expertise in at least one modern data platform (cloud and on-premises) and practical experience with at least one other.Data modelling: Expert conceptual, logical and physical modelling dimensional (Kimball), normalised (3NF) and Data Vault 2.0.Information architecture: Business glossaries, taxonomies, ontologies, canonical and semantic models, metadata, master and reference data, and data domain ownership.Data governance: DAMA-DMBOK-aligned practice data quality, lineage, classification, privacy, retention and stewardship.Engineering literacy: Advanced SQL for profiling and analysis, and working knowledge of ELT, change data capture, streaming and batch patterns to guide engineers and review their designs.Domain data: Finance (general ledger, chart of accounts, cost and profitability, management reporting) and manufacturing (plant historians, asset hierarchies, maintenance and time-series data), and the SAP or Oracle ERP structures behind them.

Requirements

Bachelor's or Master degree in Computer Science, Information Systems, Software Engineering, Engineering or related discipline.12+ years in data management, data warehousing or data engineering, including 5+ years in data architecture or data modelling roles.Lead data architect or modeler on at least two enterprise data platforms or data warehouse programs that were productionized, ideally in a consulting firm or technology vendor; finance and/or manufacturing experience is strongly preferred.Certifications such as DAMA CDMP, TOGAF or a data platform certification (Microsoft, Databricks or Snowflake) are an advantage.