Data Intelligence Lead

We are rebuilding our Data Analytics Team into a Data Intelligence Team - a team that not only delivers reports and dashboards, but also builds the company's data foundation, metric systems, self-service analytics capabilities, and AI-powered analytics workflows.
We are looking for a Data Intelligence Lead to drive this transformation.
This role is ideal for someone with a strong background in Data Analytics, Analytics Engineering, or Data Platforms, a deep understanding of business operations, proven team leadership capabilities, and a strong interest in applying AI Agents and Generative AI to accelerate decision-making across the organization.
MISSION
Build the Data Intelligence Team into a high-leverage function for the entire company through:
Trusted data
Clear metrics
Reusable dashboards and reports
Self-service analytics
AI analytics tools
Automated insights, alerts, and monitoring
Decision support for business teams and leadership
The goal is not to produce more reports, but to help the company make decisions faster, more accurately, and with less dependence on manual analysis.
WHAT YOU WILL DO1. Lead the Data Intelligence Strategy
Redesign the operating model of the Data Team, transitioning from request-based reporting to proactive decision support.
Build the roadmap for Data Platform, Analytics Engineering, Business Analytics, AI Analytics, and Data Governance.
Work directly with Leadership, Product, Sales, Marketing, Operations, and Finance teams to identify high-impact data opportunities.
Translate business goals into metrics, OKRs, dashboards, alerts, and AI workflows.
Prioritize data initiatives based on business impact rather than request volume.
2. Build the Metric Layer and Semantic Layer
Standardize key business metrics such as revenue, GMV, retention, churn, activation, conversion, CAC, LTV, gross margin, campaign performance, and operational efficiency.
Build reusable datasets, data marts, semantic models, and metric definitions.
Ensure business users, dashboards, and AI tools share a consistent understanding of data.
Document business logic, assumptions, data context, and limitations.
Eliminate inconsistencies caused by different teams calculating metrics differently.
3. Manage Data Platform and Data Quality
Build and operate the Data Warehouse, ETL/ELT pipelines, BI systems, alerting systems, and analytics infrastructure.
Ensure Data Quality, Data Security, Data Availability, and Data Governance.
Standardize processes for data modeling, testing, documentation, code review, and release management.
Collaborate with Engineering teams to improve event tracking, data contracts, and source data reliability.
Build data monitoring and data observability capabilities to detect data issues early.
4. Drive Business Analysis and Decision Support
Proactively analyze business challenges, identify insights, and provide clear recommendations.
Support teams with funnel analysis, cohort analysis, segmentation, attribution, experiment readouts, and performance reviews.
Transform recurring business questions into reusable dashboards, metrics, alerts, or AI workflows.
Enable leadership with reliable and transparent business performance monitoring systems.
5. Build AI Analytics Workflows
Lead the adoption of Generative AI, AI Agents, and AI-assisted workflows within the Data Team.
Build or guide the development of AI Analytics Tools that enable business users to access and analyze data more effectively.
Build internal data agents for use cases such as:
Natural language data querying
SQL generation and SQL review
Daily and weekly business summaries
Revenue, campaign, and product anomaly detection
Root-cause analysis
Experiment readout automation
Customer churn and expansion signal detection
Ensure AI outputs are validated, properly permissioned, source-cited, and based on trusted data.
Research and apply MCP, RAG, Vector Databases, Semantic Layers, AI Agents, and workflow automation where appropriate.
6. Build and Develop the Data Team
Manage and develop a team of Data Analysts, Analytics Engineers, and Data Engineers.
Evolve the team from a request-driven model to a proactive, high-ownership, AI-native operating model.
Establish standards for data quality, documentation, analytical rigor, AI usage, and stakeholder communication.
Coach team members to effectively leverage AI tools in their daily work.
Recruit and develop talent with a combination of business thinking, data expertise, and an automation mindset.
EXPECTED OUTCOMES IN THE FIRST 3 MONTHS
A clearly defined operating model for the Data Intelligence Team.
A prioritized roadmap for Data Platform, Analytics Engineering, and AI Analytics initiatives.
Standardization of the company's most critical business metrics.
Reduction in repetitive ad-hoc reporting and manual analysis.
Faster turnaround for answering key business questions.
Improved Data Quality, documentation, and governance.
Initial AI Analytics workflows adopted by business teams.
More reliable business performance monitoring systems for leadership.