Website:
cbre.co.in
Job details:
About the Role
CBRE has established SODA as its enterprise Data Quality platform — the operational backbone of our DQ program running across Snowflake, surfacing quality scores into Collibra, and powering a custom-built React remediation application. We are looking for a Principal Data Quality Engineer to own the platform end-to-end: from architecture and engineering standards to feature rollout and segment adoption.
This is a senior individual contributor role with solution architecture scope. You will partner with the SODA Product Lead on roadmap execution while providing the technical leadership and hands-on depth to elevate CBRE’s DQ capability from established to industry-leading — including harnessing AI and ML to move beyond rule-based quality monitoring into intelligent, predictive data quality at enterprise scale.
Why This Role Matters at CBRE
Data quality at CBRE is evolving from reactive monitoring to predictive intelligence. SODA is our established platform — and this hire takes it to the next level, embedding AI and ML into how we detect issues, prioritise remediation, and scale DQ adoption across segments. With a custom remediation application already in flight and Collibra deeply integrated, this Principal Engineer will architect the future state of data quality at one of the world’s most data-intensive companies.
Your First Mission (Months 1–6)
Own the architecture and delivery of CBRE’s next-generation DQ monitoring framework — extending SODA coverage to priority segments, hardening the Collibra integration, and shipping the first AI/ML-powered anomaly detection capability on top of our Snowflake estate. Prove that intelligent, scalable DQ is achievable at enterprise scale.
From there, you’ll define the engineering standards that govern how every segment at CBRE instruments, monitors, and remediates data quality — and build the platform capabilities that make adoption the path of least resistance.
Key Responsibilities
Platform Ownership & Architecture
Own the end-to-end technical architecture of CBRE’s SODA DQ platform — including scan orchestration, alerting, SLA tier management, and Collibra integration.
Define and evolve the solution architecture for DQ monitoring across CBRE’s Snowflake data estate, ensuring scalability as new domains and segments onboard.
Lead architecture decisions for the React-based DQ remediation application — driving feature evolution, performance, and integration with SODA and Collibra workflows.
Establish and govern engineering standards for SodaCL check authoring, naming conventions, scan scheduling, and data contract definitions.
AI/ML-Augmented Data Quality
Architect and implement AI/ML-powered DQ capabilities — including anomaly detection, pattern-based quality scoring, and predictive issue identification — to move CBRE’s DQ program from rule-based to intelligence-driven.
Evaluate and integrate SODA’s AI features alongside complementary ML approaches (e.g., statistical profiling, unsupervised anomaly detection on Snowflake) to reduce manual check authoring burden at scale.
Drive the evolution of the React remediation application to surface AI-generated DQ insights — prioritising issues by business impact, predicting recurrence, and recommending remediation actions.
Feature Delivery & Roadmap Execution
Partner with the SODA Product Lead to translate the DQ product roadmap into engineered, production-ready features.
Lead the design and build of new DQ platform capabilities — including advanced check patterns, automated remediation triggers, and self-serve onboarding tooling for segment teams.
Drive continuous improvement of the remediation application — extending its capability to surface actionable DQ insights to data stewards, engineers, and business stakeholders.
Segment Rollout & Adoption
Lead the technical delivery of SODA rollouts to key CBRE segment applications, working with segment engineering and data stewardship teams.
Build scalable onboarding patterns — check libraries, domain-specific templates, and automation accelerators — that reduce time-to-value for each new segment.
Define and track DQ adoption metrics per segment; escalate adoption blockers to the Product Lead with clear remediation plans.
What You Bring
:
Must-Haves
12+ years in data engineering, data quality, or data platform roles with demonstrable depth in DQ program design and implementation.
Principal or Staff Engineer-level experience — with a track record of owning architecture decisions, not just executing them.
Hands-on SODA expertise (or equivalent: Great Expectations, Monte Carlo, dbt tests) — SODA experience strongly preferred.
Solution architecture experience — ability to design end-to-end DQ solutions across ingestion, transformation, and serving layers.
Strong Snowflake expertise; experience implementing DQ monitoring across a multi-layer data architecture (Bronze/Silver/Gold or equivalent).
Experience applying ML or statistical techniques to DQ problems — anomaly detection, distribution drift, outlier identification, or automated profiling.
Experience owning or significantly contributing to a React-based or similar front-end application in a data or platform context.
Deep understanding of DQ dimensions, data contracts, and SLA/SLO design.
Nice-to-Haves:
Experience integrating DQ platforms with Collibra — linking quality metrics to governance artifacts, data products, and stewardship workflows.
Familiarity with AI-assisted metadata and quality tooling — within SODA, Collibra, or the broader modern data stack.
Familiarity with LLM-assisted data quality — automated business rule inference, check generation from data dictionaries, or natural language DQ reporting.
Familiarity with orchestration tooling (Airflow, dbt) for scan scheduling and pipeline-triggered checks.
Knowledge of commercial real estate data domains — property, lease, transaction, client — a genuine differentiator at CBRE.
Click on Apply to know more.