Anshul Verma

Anshul Verma is a data engineering and analytics leader who has built his career at the intersection of enterprise data platforms and cloud-native execution. Over more than eight years, Verma has consistently operated beyond routine BI delivery—driving initiatives that modernize how organizations collect, validate, observe, and activate data at scale. His work is distinguished by a platform mindset: designing systems that reduce operational friction, improve reliability, and convert analytics into faster, higher-confidence decision-making.

In senior roles spanning data engineering, business intelligence, and analytics infrastructure, Verma’s core specialization has been Enterprise Data Platform Engineering with emphasis on cloud cost optimization, large-scale ETL automation, and observability. He has repeatedly taken ownership of high-impact programs that required cross-functional leadership across engineering, analytics, product, and operations—often transforming fragmented, manual workflows into governed, automated systems.

At Zeta Global, Verma led a comprehensive AWS data warehousing optimization effort as a Senior Business Intelligence Manager, re-architecting storage and retrieval patterns to improve both performance and cost-efficiency. By redesigning warehouse schema and restructuring pipelines, he delivered substantial measurable outcomes—reducing data retrieval time by 78% while generating approximately $1M in annual savings. The work reflected not just tuning, but an architectural reframe: treating cost, latency, and scalability as first-class platform requirements rather than after-the-fact constraints.

Verma’s data reliability work demonstrates the same engineering depth. He designed and implemented an automated observability framework that unified workflow orchestration, BI metadata, and incident-response tooling—integrating Airflow, Looker API, and Opsgenie to detect data quality issues proactively and trigger targeted alerts. This replaced manual monitoring with automation and intelligence, enabling 95% faster incident response and reducing data delays by 75%. Complementing this, he initiated and led a broader enterprise data governance program—introducing standardized validation rules, quality checks, and reliability controls in environments that lacked formal guardrails—cutting data errors by 60% and elevating reporting integrity.

At LiveIntent, Verma’s scope extended from reliability and pipelines to revenue-impacting product analytics. He architected scalable data infrastructure and automated ETL powering a flagship “Email Reactivation” product—handling identity-graph inputs and model-scored outputs while enabling near-real-time reporting and client-facing dashboards. These systems supported measurable business outcomes, including 178% ROI improvement and 200% engagement lift, reflecting his ability to design data platforms that directly influence product performance and commercial results.

Verma also expanded traditional BI into applied machine learning by developing and productionizing a Python-based LookAlike Audiences framework. He engineered pipelines, feature logic, and scoring workflows to operationalize predictive segmentation, driving 155% performance improvement and increasing client spend by 66%. Alongside advanced modeling, he improved organizational throughput through enablement: building a self-service Looker environment using governed LookML models and reusable reporting templates, reducing report creation time by 75% and saving 15+ team hours weekly. For high-volume needs, he engineered Python and Spark pipelines to process log-level and real-time datasets, integrating big-data constructs into production analytics operations to support reporting, revenue strategy, and cross-team insights.

Across these initiatives, Verma’s profile is consistent: he builds durable systems that scale—not only compute, but also organizational capacity. His work blends engineering rigor (automation, observability, governance) with business-facing outcomes (cost savings, ROI lift, performance improvement), positioning him as a modern data platform leader whose impact is measurable, repeatable, and foundational.