Data Architect Role

Data Architect

Data Architects shape how an organization represents, connects, governs, and uses data across systems. They translate business processes, information needs, and technical constraints into conceptual, logical, and physical models that teams can implement and evolve.

The work spans relational databases, analytical platforms, data lakes, warehouses, and other fit-for-purpose stores. You assess a fragmented current state, define a target architecture, establish integration and access patterns, and decide where standardization matters most. Good decisions account for data shape, access patterns, scale, consistency, security, resilience, cost, and the realities of migration.

This is a design and decision role. Data Architects work closely with data engineers, database developers, database administrators, security teams, software engineers, analysts, and business stakeholders. They make tradeoffs visible, preserve traceability from requirements to implementation, and help teams change an estate without losing integrity or control.

· Conceptual, logical, and physical data models for operational and analytical systems.

· Target-state architectures for warehouses, lakes, lake houses, relational databases, and specialized stores.

· Canonical models, integration patterns, data contracts, naming standards, and schema conventions.

· Architecture decision records, transition roadmaps, and current-state or future-state diagrams.

· Governance-aware designs for metadata, lineage, access, retention, quality, and lifecycle management.

You see both the whole information system and the details that make a model implementable. You can identify duplicated concepts, unclear ownership, brittle dependencies, and hidden assumptions, then turn them into decisions that teams can act on.

You are precise without becoming rigid. You listen for how people actually use data, explain tradeoffs to technical and nontechnical audiences, and know when a shared standard creates value and when a local design is the better choice.

· Experience with data modeling, schema design, normalization, dimensional modeling, and model evolution.

· Judgment across relational, analytical, document, graph, key-value, and vector-capable data stores.

· Understanding of integration patterns, data contracts, metadata, lineage, data quality, and governed access.

· Ability to evaluate architecture choices against security, resilience, performance, scalability, cost, and migration risk.

· Clear visual, written, and verbal communication across engineering, security, product, and business teams.