Data Architect

Randstad
United States
21 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
8 years minimum
Compensation
$176,800.0 - $193,440.0
Working hours
Regular working hours
Job source

Tech stack

BigQuery Data Architecture Data Governance Data Warehousing Digital Assets Dimensional Modeling Meta-Data Management Metadata Standards Enterprise Data Management Google Cloud Data Lineage Collibra
+1 more
Star Schema

Job description

We have an immediate need for an EDP Data Architect The EDP Data Architect is a senior, hands-on contract role responsible for the enterprise data architecture, modeling standards, and conformed data model across the Enterprise Data Platform (EDP). EDP is built fully on Google Cloud Platform on a medallion architecture and supports both the migration of workloads from our legacy on-premises EDW and the build-out of net-new enterprise data products across marketing, customer, and other business domains. The Data Architect operates at the enterprise level, defining and maintaining the standards, canonical model, and governance architecture that solution architects and engineering teams build against.

This role sits within the Platform Enablement team and is the counterpart to the workstream-level Solution Architect roles: where Solution Architects apply platform standards within a given use case, the Data Architect sets and owns those standards across the platform. The role works closely with data engineers, solution architects, and data governance stakeholders, and is central to standing up consumption gold layer, the platform’s data catalog and data product marketplace., * Own and maintain the enterprise data architecture for EDP - the patterns, principles, and standards governing how data is structured, integrated, and made available across all domains.

  • Define and maintain modeling and architecture standards across the medallion layers (bronze, silver, gold) so designs stay consistent regardless of which team or solution architect executes them.
  • Own the canonical (conformed) data model - the enterprise definitions for core business entities such as customer, product, order, and inventory - keeping them consistent across marketing, supply chain, and all other domains.
  • Own the star schema and dimensional modeling standards in the gold layer, including fact and dimension design, conformed dimensions, grain definitions, and slowly-changing-dimension patterns.
  • Set the dimensional and conformed-layer standards that solution architects execute against, preventing shared entities and metrics from drifting into inconsistent definitions across workstreams.
  • Lead the technical implementation of the defined data governance framework, translating governance policy and ownership models into working architecture on the platform.
  • Define how metadata, ownership, and classification are captured and enforced, ensuring the right metadata is captured at the right points in the pipeline.
  • Design the data lineage architecture - how lineage is captured, maintained, and surfaced - connecting it to pipeline instrumentation rather than treating it as documentation.
  • Define and operate the data product certification tiers within Trailhead (gold layer), including the criteria and publishing workflow that move a data product between tiers.
  • Establish the catalog and metadata standards that keep gold layer accurate, complete, and trustworthy as new data products are published.
  • Review architectural and modeling decisions across workstreams, confirming designs conform to enterprise standards before they are built.
  • Establish reference architectures, pattern libraries, and decision records so standards are easy to adopt and do not slow delivery.
  • Provide hands-on architectural guidance to engineering teams and solution architects on design and implementation decisions across the platform.
  • Document standards and architecture so the platform’s design knowledge is retained within the internal team over time.

Requirements

8-10+ years of data architecture experience with demonstrated enterprise-scope ownership, not single-project or single-domain scope.

Hands-on Google Cloud Platform experience with deep, direct BigQuery expertise - partitioning and clustering strategy, dimensional design, performance and cost optimization, and dataset-level access control.

Proven ownership of a canonical / conformed enterprise data model and of dimensional (star schema) modeling standards used across multiple consuming teams.

Experience designing data governance architecture - metadata, classification, ownership, and lineage - as technical implementation rather than policy alone.

Hands-on experience with a data catalog or metadata management platform such as Dataplex, Collibra, or Alation.

Track record of setting and enforcing standards across multiple teams through reference patterns and lightweight review rather than heavy approval gates.

Able to operate independently at an architect level in a contract engagement and establish credibility and direction quickly.

Preferred Qualifications

Experience on a large-scale EDW-to-cloud migration, particularly to Google Cloud Platform and BigQuery.

Retail, e-commerce, or consumer domain experience.

Familiarity with medallion or lakehouse layer design and conformed-dimension strategies in a cloud data warehouse.

Experience standing up or maturing a data catalog or data product marketplace, including certification or trust-tier models.

Exposure to data product operating models covering product, business, and technical ownership of data assets.

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