Senior Director, Data Engineering & Business Intelligence

Wpromote

4d•$140,000 - $195,000•Remote

About The Position

We are looking for a Senior Director of Data Engineering & Business Intelligence to lead Wpromoteâ€™s data platform, client data products, and analytics innovation efforts. In this role, you will oversee a team of Data Engineers, business intelligence engineers, and Reporting Solutions specialists while working cross-functionally to design, build, and deliver reliable, scalable, automated data infrastructure that powers reporting, analytics, and strategic decision-making across both Wpromote and our clientsâ€™ businesses. This leader will help evolve our reporting ecosystem by integrating new data sources, modernizing reporting experiences, and driving innovation in conversational analytics and real-time data delivery. The ideal candidate is a strategic thinker and people leader who thrives on building scalable data products, improving operational efficiency, and developing high-performing teams. This leader will operate in a high-visibility client-facing function and lead the delivery of data engineering and reporting, with specific responsibilities. At Wpromote, we believe that great work is only possible with great people. Our goal is to build a better, more inclusive work environment and support our people at every stage of their careers by prioritizing a strong work-life balance through our policies and benefits listed below. As a Best Place to Work according to both Ad Age and Glassdoor and Adweekâ€™s Fastest Growing Digital Agency, we are moving fast to expand our teams and bring new experts into the fold to keep pushing the boundaries of whatâ€™s possible in marketing.

Requirements

12+ years of experience in data engineering, BI, or analytics within an advertising agency environment (preferred) and/or client (Brand) environment
Experience working with marketing, sales, or digital media data (Google, Adobe, Meta, Salesforce, etc.)
Background in digital analytics platforms (e.g., Google Analytics, Adobe Analytics)
5+ years in a leadership or management role
Proven experience building scalable data pipelines and modeling data using SQL and tools like dbt or similar
Expertise in cloud data platforms (e.g., BigQuery, Snowflake, Redshift) and orchestration tools (e.g., Airflow, Cloud Composer)
Strong understanding of ETL/ELT workflows and data warehousing best practices
Hands-on experience integrating with APIs and managing large-scale data ingestion
Deep familiarity with BI platforms such as Looker, Tableau, Power BI, or similar
Excellent communication and stakeholder management skills, including the ability to translate business needs into technical solutions
Demonstrated experience in implementing and maintaining rigorous data QA and governance processes
An understanding of modern data architecture patterns (e.g., event streaming, data lakehouses)
Experience in deploying predictive analytics and AI models into engineering and reporting workflows

Nice To Haves

Intermediate to advanced experience in Python/ R
Familiarity with conversational analytics, natural language interfaces, or chatbot integration for BI
Familiarity with project management tools such as JIRA, Asana, or Monday.com
Prior experience in a consulting environment, especially in marketing or technology services

Responsibilities

Leading and mentoring a team of data engineers, business intelligence engineers, and reporting solution engineers to build and maintain accurate, dependable, and automated data pipelines and data models
Overseeing the architecture and design of our BI ecosystem across internal and client-facing environments while remaining hands-on in the design and development of client-facing solutions
Driving innovation by exploring new APIs, data providers, and integration approaches to expand our data capabilities
Serving as a coach-player across client work, guiding technical approach and best practices while directly contributing to pipeline development, data modeling, QA, troubleshooting, and delivery
Partnering with analytics, strategy, and engineering stakeholders to define and deliver on data and reporting requirements
Designing scalable and reusable data models using dbt and BigQuery, optimized for reporting, forecasting, analytics, and client-specific customization
Leading the development of modern reporting interfaces and conversational analytics tools that empower users to self-serve insights
Evolving and enforcing QA processes, data governance standards, documentation practices, and engineering best practices to improve reliability and consistency across client deliverables
Leading data modernization by utilizing AI-assisted workflows and automation to manage client deliverables efficiently and effectively
Managing stakeholder expectations, project timelines, prioritization, and technical tradeoffs for cross-functional data initiatives while ensuring high-quality execution on active client work