Senior Architect, Data Classification and Governance
- Pay Rate: $100 – $120/hour, depending on experience
- Contract Length: Contract runs until March 31, 2027 (possibility of extension)
- Location: Calgary (hybrid)
Raise is currently hiring a contract team member on behalf of our client. They’re expanding their team to meet growing needs, making this a unique opportunity to work with an industry leader.
Description
Our client is seeking a Senior Architect, Data Classification and Governance to own the technology architecture standard for data governance across a heterogeneous estate in transition. This role advises developers, data architects, and data modellers on how to build classification, access control, retention, and quality directly into design and delivery decisions, working at the level of specific schema, model, and control choices rather than high-level principles. The successful candidate will define the target governance toolchain architecture, assess the current state of the estate, and prove each architecture standard with a working reference implementation rather than a document alone.
This is a hands-on technical architecture role that spans structured, unstructured, and geospatial data, requiring both deep governance expertise and the credibility to sit in design reviews and give specific, implementable direction to development teams.
Responsibilities
- Author the technology architecture standard for data governance, covering classification, metadata and tagging, access control, retention, quality instrumentation, lineage, and reference data change control across structured, unstructured, and geospatial data
- Advise developers, data architects, and data modellers on building governance into design and delivery at the level of specific schema, model, and control decisions; participate in design reviews and provide written governance assessments of in-flight work
- Define the classification approach for each data shape: structured data in custom application databases and the lakehouse, unstructured content in document and file estates, and geospatial data at the feature and attribute level
- Define the target governance toolchain architecture across Microsoft Purview and Databricks Unity Catalog, including which is authoritative for classification, lineage, and glossary, how the two federate, and what neither covers natively
- Assess the current estate to determine what is governed centrally, what is governed at source, and what is deferred; sequence governance against what the estate supports today versus what requires platform change
- Advise the internal team that owns business data governance standards on technology feasibility, implementation effort, and downstream cost; identify gaps where those standards are silent, ambiguous, or unimplementable, and bring specific recommendations
- Prove each architecture standard with a reference implementation or proof of concept so teams receive a working pattern rather than a document to interpret
- Work with Data Architecture to embed governance into data contracts, schema ownership, and ingestion patterns so new data lands governed by default
- Build internal staff capability through design coaching, pattern walkthroughs, and developer-facing documentation; transfer each published standard to a named owner of record
- Define the data quality technology architecture, including how rules are authored, where they execute, how failures are handled, and how quality is measured and reported, covering the full path from data submission or capture through ingestion to consumption
- Establish where quality is enforced versus observed, including validation at point of submission/capture, contract enforcement at ingestion boundaries, and monitoring in the lakehouse; advise on the cost tradeoff between rejecting bad data early versus remediating downstream
Required Skills
- Demonstrated experience translating data governance requirements into technology architecture that development teams actually built from; experience limited to framework or policy authorship will not meet this requirement
- Demonstrated experience governing a heterogeneous estate in transition, including deciding what to govern centrally versus at source and sequencing realistically against legacy constraints
- Unstructured data governance: sensitivity and retention labelling, scanning and auto-classification, and DLP across document and file estates
- A proven approach to classification in custom-built applications, covering classification metadata in the application schema, capture at source, and propagation through change data capture into the Lakehouse
- Ability to sit in a design review with developers and modellers and give specific, implementable direction on the design in front of them
- Strong technical writing; standards must be usable by a developer without the author present
- Demonstrated design of a data quality framework that reached production, including rule authoring, execution, quarantine handling, and measurement; a scorecard without enforcement will not meet this requirement
- Experience with data quality rule execution tooling and the tradeoffs between options, such as Databricks Lakehouse Monitoring, DLT or pipeline expectations, Microsoft Purview data quality, and open source frameworks
Recommended Skills
- Geospatial data governance: classification and access control at the feature and attribute level, treatment of spatial file formats as governance objects, spatial metadata standards, and generalization or masking of sensitive locations as a privacy control
- Hands-on Microsoft Purview: data map, scanning and classification rules, sensitivity labels, glossary, lineage, and integration with non-Microsoft sources
- Hands-on Databricks Unity Catalog: catalogs, schemas, ownership, tags, grants, row and column level security, dynamic masking, audit logs, system tables
- Experience with confidential or restricted data management
- Change data capture pipelines, ideally Databricks Lakeflow Connect or equivalent
- Azure, GitHub, and CI/CD for schema and policy as code
- ROT analysis and duplicate detection across structured and unstructured content
Deliverables
- Target governance toolchain architecture across Microsoft Purview and Databricks Unity Catalog, identifying the authoritative source for classification, lineage, and glossary, and where neither tool provides coverage
- Data classification technology architecture standard addressing structured, unstructured, and geospatial data, including the approach for custom-built applications
- Access control technology architecture standard mapping classification to enforced controls at the application, database, Lakehouse, and geospatial layers
- Metadata, lineage, and data quality technology architecture standards
- Assessment of data quality tooling options across the target toolchain, with a recommendation on where rules are authored and where they execute
Education/Certification Requirements
- University degree in a related discipline (data management, computer science, information management, geomatics, analytics) or equivalent demonstrated experience. Professional certification such as CDMP, CIMP, or DGSP is recommended. Demonstrated implementation experience is weighted above certification.
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