Associate Director, Data Intelligence

About the Company

Key Responsibilities

Team Leadership & Operations 

Lead, mentor, and develop a team of data engineers, analysts, integration engineers, PowerBI developers and AI/ML Engineers. 

Provide direction, coaching, and performance feedback to team members while supporting their career growth and technical development. 

Foster a culture of continuous improvement, collaboration, and innovation. 

Implement and maintain a comprehensive data strategy aligned with business goals. 

Manage intake, prioritization, and delivery of data-related requests, projects, and enhancements 

Establish SLAs, operational metrics, and reporting cadences to communicate team performance to leadership 

Manage resource planning, workload distribution, project delivery, hiring, onboarding and support activities across the data intelligence function 

Foster a collaborative, data-driven culture aligned with organizational goals and values 

Partner closely with cross-functional teams including ecommerce, Data Engineering, Business Insights, Enterprise Applications, Infrastructure, Security, and QA. 

Provide status updates to leadership on operational health, key issues, risks, priorities, and progress 

Data Warehouse Management 

Oversee the design, development, and operational maintenance of the enterprise Data Warehouse architecture 

Manage data ingestion pipelines, ETL/ELT processes, and data transformation workflows 

Ensure data integrity, consistency, and availability across all warehouse environments (Dev, QA, Prod) 

Provide technical leadership in SQL development, ETL/ELT processes, data modeling, database design, and performance tuning. 

Evaluate and recommend new technologies, tools, and methodologies to enhance warehouse performance 

Support data warehouse enhancements, new data source onboarding, reporting needs, and business-critical data initiatives.  

Implement and enforce data quality controls, lineage, metadata management, and documentation. 

API Integrations 

Lead the execution of API integrations connecting internal and third-party systems  

Collaborate with engineering and vendor teams to design, build, and monitor RESTful and SOAP API integrations 

Establish standards for API documentation, versioning, error handling, and data validation 

Drive improvements in integration support processes, incident response, and operational reliability.  

Partner with application, integration, ERP, ecommerce, and business teams to ensure APIs and data integrations are stable and functioning as expected.  

Assist to troubleshoot and resolve integration failures, ensuring minimal disruption to downstream systems and reporting 

Analytics & Power BI 

Manage and support analytics requests related to Power BI reports, dashboards, datasets, and business reporting needs.  

Partner with business users to understand reporting requirements, data definitions, KPI needs, and analytics priorities.  

Champion self-service analytics by establishing best practices, data models, and certified datasets 

Provide training and enablement resources to empower business users across the organization 

Support report enhancements, dashboard issue resolution, and dataset refresh failures.  

Promote consistency in reporting, data definitions, and analytics best practices across the organization.  

Data Governance & Security 

Lead the overall data intelligence strategy aligned with business priorities, technology roadmap, and enterprise goals. 

Partner with Security, Compliance, and Business teams to enforce data governance policies and access controls 

Ensure compliance with applicable data privacy regulations (e.g., GDPR, CCPA) and SOX/ITGC controls where applicable 

Manage audit readiness, change management, and segregation of duties across environments. 

Maintain comprehensive data dictionaries, lineage documentation, data cataloging, and metadata management practices 

AI & Machine Learning 

Lead and support the organization’s advancement into AI, machine learning, predictive analytics, and intelligent automation. 

Identify practical AI/ML use cases that can improve business outcomes, operational efficiency, customer experience, and decision-making. 

Partner with data scientists, engineers, analysts, and business teams to develop scalable AI/ML solutions. 

Help establish the foundation required for AI readiness, including clean data pipelines, governed datasets, quality controls, and reusable data assets. 

Support the evaluation and implementation of AI-enabled analytics tools, machine learning platforms, and automation capabilities. 

Promote responsible AI practices, including data privacy, model transparency, risk management, and ethical use of data. 

Qualifications and Requirements

• Bachelor's or Master's degree in Computer Science, Information Systems, or a related field. 
• 18+ years of progressive experience in Information Technology 
• 10+ years of hands-on experience with Data Warehouse architecture, development, and operations 
• 7+ years in a people management or team lead capacity within a data or analytics organization 
• Deep expertise in cloud data platforms such as Snowflake, Azure Synapse, Amazon Redshift, or SQL Server and modern data architectures 
• Proficient in ETL/ELT development using tools such as Azure Data Factory, Informatica, Matillion, dbt, or similar. 
• Strong experience designing and managing API integrations (REST, SOAP, JSON, XML, OAuth) 
• Proficiency with Power BI or Analytic platforms including data modeling (DAX, Power Query), report development, gateway management, and workspace administration 
• Expertise in SQL for complex querying, performance tuning, and data analysis 
• Demonstrated ability to manage concurrent projects, prioritize effectively, and communicate with executive stakeholders 
• Strong analytical, problem-solving, and critical thinking skills 
• Familiarity with data mesh, data lakehouse, or medallion architecture patterns 
• Ability to work in a fast-paced and dynamic environment. 
• Excellent leadership and team-building capabilities. 
• Excellent communication and collaboration skills. 
• Exposure to machine learning pipelines or AI-driven analytics use cases 
• Knowledge of ITIL frameworks and enterprise IT service management practices 
• Experience with Snowflake