Principal Data Quality Engineer

Job description

**About The Role** We are looking for a Principal Data Quality Engineer to ownthe platform end-to-end: from architecture and engineering standards to featurerollout and segment adoption. This is a senior individual contributor role with solutionarchitecture scope. You will partner with the SODA Product Lead on roadmapexecution while providing the technical leadership and hands-on depth toelevate CBRE’s DQ capability from established to industry-leading — includingharnessing AI and ML to move beyond rule-based quality monitoring intointelligent, predictive data quality at enterprise scale. **What You’ll Do** **Platform Ownership & Architecture** - Own theend-to-end technical architecture of CBRE’s SODA DQ platform — including scanorchestration, alerting, SLA tier management, and Collibra integration. - Define andevolve the solution architecture for DQ monitoring across CBRE’s Snowflake dataestate, ensuring scalability as new domains and segments onboard. - Leadarchitecture decisions for the React-based DQ remediation application — drivingfeature evolution, performance, and integration with SODA and Collibraworkflows. - Establishand govern engineering standards for SodaCL check authoring, namingconventions, scan scheduling, and data contract definitions. **AI/ML-Augmented Data Quality** - Architectand implement AI/ML-powered DQ capabilities — including anomaly detection,pattern-based quality scoring, and predictive issue identification — to moveCBRE’s DQ program from rule-based to intelligence-driven. - Evaluateand integrate SODA’s AI features alongside complementary ML approaches (e.g.,statistical profiling, unsupervised anomaly detection on Snowflake) to reducemanual check authoring burden at scale. - Drive theevolution of the React remediation application to surface AI-generated DQinsights — prioritising issues by business impact, predicting recurrence, andrecommending remediation actions. **Feature Delivery & Roadmap Execution** - Partnerwith the SODA Product Lead to translate the DQ product roadmap into engineered,production-ready features. - Lead thedesign and build of new DQ platform capabilities — including advanced checkpatterns, automated remediation triggers, and self-serve onboarding tooling forsegment teams. - Drivecontinuous improvement of the remediation application — extending itscapability to surface actionable DQ insights to data stewards, engineers, andbusiness stakeholders. **Segment Rollout & Adoption** - Lead thetechnical delivery of SODA rollouts to key CBRE segment applications, workingwith segment engineering and data stewardship teams. - Buildscalable onboarding patterns — check libraries, domain-specific templates, andautomation accelerators — that reduce time-to-value for each new segment. - Define andtrack DQ adoption metrics per segment; escalate adoption blockers to theProduct Lead with clear remediation plans. **Must-Haves** **What You’ll Need:** - 12+ yearsin data engineering, data quality, or data platform roles with demonstrabledepth in DQ program design and implementation. - Principalor Staff Engineer-level experience — with a track record of owning architecturedecisions, not just executing them. - Hands-onSODA expertise (or equivalent: Great Expectations, Monte Carlo, dbt tests) —SODA experience strongly preferred. - Solutionarchitecture experience — ability to design end-to-end DQ solutions acrossingestion, transformation, and serving layers. - StrongSnowflake expertise; experience implementing DQ monitoring across a multi-layerdata architecture (Bronze/Silver/Gold or equivalent). - Experienceapplying ML or statistical techniques to DQ problems — anomaly detection,distribution drift, outlier identification, or automated profiling. - Experienceowning or significantly contributing to a React-based or similar front-endapplication in a data or platform context. - Deepunderstanding of DQ dimensions, data contracts, and SLA/SLO design. **Nice-to-Haves** - Experienceintegrating DQ platforms with Collibra — linking quality metrics to governanceartifacts, data products, and stewardship workflows. - Familiaritywith AI-assisted metadata and quality tooling — within SODA, Collibra, or thebroader modern data stack. - Familiaritywith LLM-assisted data quality — automated business rule inference, checkgeneration from data dictionaries, or natural language DQ reporting. - Familiaritywith orchestration tooling (Airflow, dbt) for scan scheduling andpipeline-triggered checks. - Knowledgeof commercial real estate data domains — property, lease, transaction, client —a genuine differentiator at CBRE.