Consultant, Data Quality Analytics

Consultant, Data Quality Analytics
Data Science is all about breaking new ground to enable businesses to answer their most urgent questions. Pioneering massively parallel data-intensive analytic processing, our mission is to develop a whole new approach to generating meaning and value from petabyte-scale data sets and shape brand new methodologies, tools, statistical methods, and models. What’s more, we are in collaboration with leading academics, industry experts, and highly skilled engineers to equip our customers to generate sophisticated new insights from the biggest of big data.
What you’ll achieve:
As a Consultant, you will lead high-impact data quality programs that strengthen monitoring, validation, and defect remediation. You will automate checks, rollout policies, and measure improvements with quality KPIs across critical data products. You will collaborate with engineering and analytics teams to deliver scalable solutions and robust documentation.
Prove that the Knowledge & Intelligence layer's data can be trusted - and keep proving it. This is the data-trust and AI-readiness role for the governed knowledge spine that grounds AI agents on verified, provenance-backed facts. You trace where every fact came from, measure its quality and freshness, and turn those signals into the dashboards and evidence that show the layer meets its AI-ready data standard - continuously, not as a one-time sign-off. When trust, lineage, or quality drifts, you're the first to see it and the one who makes it visible to everyone else.
Join us to do the best work of your career and make a profound social impact as a Consultant on our Data Quality Analytics team.
Take the first step towards your dream career
Every Dell Technologies team member brings something unique to the table. Here’s what we are looking for with this role:
Essential Requirements
Strong data-analysis background - SQL-fluent, comfortable tracing data across systems and turning messy signals into clear, trustworthy views. Comes with 12-15 years of experience.
Hands-on experience building report and monitoring dashboards (Splunk, Elastic / Kibana, Grafana, or similar). Experience with data quality, lineage, or data governance - measuring completeness, freshness, and drift, and evidencing it.
Familiarity with data provenance and PII / sensitivity concepts - enough to report on them accurately.
Comfort with knowledge graphs and semantic data - the substrate you'll trace and query (SPARQL a plus).
Strong communication - you make trust and readiness legible to engineers, auditors, and leadership alike.
Desirable Requirements
Bachelor’s degree in Data Science, Computer Science, Statistics, or related field.
Certifications or vendor credentials for data quality or cloud data platforms.
You will:
Lineage & traceability. Trace every served fact back to its source and transformations - the record that makes the layer explainable and auditable.
Quality signals. Measure completeness, freshness, and schema drift across the layer's data products, and track them against their SLAs.
Active-metadata dashboards. Build and own the dashboards (Splunk, Elastic / Kibana, SQL) that keep trust, freshness, and quality signals live for the engineering teams and for leadership.
AI-readiness evidence. Assemble the conformance evidence that shows each data product meets the AI-ready data standard - at every phase gate, across all its dimensions.
Trust & compliance reporting. Surface trust-tier coverage, provenance completeness, and PII / sensitivity-labeling status - the evidence audit and governance rely on. Grounding discipline. Nothing ships ungrounded; you flag anything that can't be traced back to a source.

Work arrangement
No