Manager, Data Architecture

Dotdash Meredith•New York, NY

1d•Hybrid

About The Position

The Manager, Data Architecture, Revenue Analytics leads the design, governance, and evolution of the Ad Sales data ecosystem while directly managing the Sales Systems Engineer and BI Engineer. This role combines architectural leadership with people management accountability, ensuring that revenue data systems are scalable, trusted, and aligned with business priorities. The role is responsible for setting technical direction, prioritizing team work-streams, developing talent, and ensuring cross-functional alignment across Sales, Finance, RevOps, and Technology. This is both a strategic leadership role and a hands-on architectural steward for revenue data systems. This position is hybrid in-office, with the ability to work remotely for up to 2 days per week.

Requirements

Bachelor’s degree in a related field or equivalent professional experience preferred.
8+ years in data engineering, architecture, analytics engineering, or BI
2+ years managing or leading technical contributors (formal or informal leadership)
Advanced SQL and deep data modeling expertise
Experience with modern cloud data platforms (Snowflake, BigQuery, Redshift, Databricks)
Strong understanding of ELT/ETL patterns and orchestration
Experience supporting BI/reporting ecosystems
Working knowledge of governance and metadata management
Strong leadership skills
Ability to influence executive stakeholders
Strong prioritization and decision-making skills
Clear communication across technical and business audiences
Strong documentation discipline

Responsibilities

Own the architectural vision for revenue data systems
Approve and guide conceptual, logical, and physical data models
Define standards for modeling, integration, and data consumption
Ensure architecture supports scale, performance, and evolving business needs
Reduce fragmentation by promoting shared patterns and semantic consistency
Oversee system alignment across CRM, OMS, finance, SaaS tools, and warehouse platforms
Define system-of-record boundaries and data ownership
Ensure implementation fidelity between architectural design and execution
Guide ingestion, transformation, storage, and BI access patterns
Establish clear metric definitions and semantic models
Partner with Data Ops and Governance teams on lineage, metadata, and access controls
Reduce metric inconsistencies and reporting ambiguity
Ensure compliance with privacy and security standards
Serve as senior data advisor to Sales, Finance, and GTM leadership
Translate business priorities into technical roadmaps
Drive alignment between analytics capabilities and revenue strategy
Prioritize investments based on measurable business impact