Data Intelligence Lead

With ~200 employees, OpenCommerce Group is a leading technology organization that offers e-commerce products with offices in China, and Hanoi. We are fortunate to have a team that can target vast and vibrant markets, with the benefit of ten years in the eCommerce industry and over one million online sales sector customers around the world. most nations in the world, such as the United States and China. The aim of the OpenCommerce Group is to create a product ecosystem to facilitate and improve e-commerce in general, as well as cross-border commerce in particular, and to serve as a launching pad for entrepreneurs. Starts expands and performs in the online world. We're expanding rapidly, and we're searching for top talent to help us create a global Commerce Community. Join OpenCommerce Group to expand the scope of your work.

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 DO

1. 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.

REQUIREMENTS

Experience

• Experience in Data Analytics, Analytics Engineering, Data Platforms, Data Engineering, BI, or Data Consulting.
• Experience managing or leading Data Teams.
• Experience solving real business problems using data.
• Experience working directly with leadership and business stakeholders.
• Experience building or operating Data Warehouses, BI systems, data marts, semantic models, or self-service analytics platforms.

Business & Leadership

• Strong business thinking and the ability to connect data initiatives with revenue, growth, cost optimization, product, and operations outcomes.
• Ability to challenge ambiguous business questions and translate them into measurable analytical problems.
• Strong communication skills across business teams, engineering teams, and leadership.
• Strong prioritization skills and the ability to avoid low-impact reporting work.
• High ownership, systems thinking, and the ability to lead organizational change.

Technical

• Strong SQL proficiency.
• Solid understanding of Data Warehousing, Data Modeling, ETL/ELT, Data Quality, and Data Governance.
• Experience with BI tools such as Metabase, Power BI, Looker, Tableau, or equivalent.
• Experience with dbt or similar analytics engineering workflows.
• Proficiency in Python and Git.
• Experience with AWS, Redshift, BigQuery, Snowflake, PostgreSQL, ClickHouse, or equivalent technologies.
• Ability to design reusable datasets, semantic models, data marts, and reporting layers.

AI & Automation

• Experience with, or strong interest in, applying AI to analytics workflows.
• Understanding of AI Analytics Tools, AI Agents, AI Workflows, or AI-powered automation.
• Knowledge of Prompt Engineering, MCP, RAG, Vector Databases, or LLM tool-calling is a plus.
• Ability to evaluate AI outputs in terms of accuracy, reliability, security, and business usefulness.
• Proactively researches and adopts new tools to improve Data Team productivity.

NICE TO HAVE

• Experience building natural language analytics or “chat with data” tools.
• Experience with Semantic Layer and Metric Layer implementations.
• Experience with data observability, lineage, or data contracts.
• Experience in product analytics, marketing analytics, sales analytics, or ecommerce analytics.
• Experience building business review systems for leadership teams.
• Experience building internal AI Agents or workflow automation solutions.
• Previous experience working in startups or multi-product companies.

ARE YOU AI-NATIVE?

You are not just a Reporting Lead.

You are someone who wants to build a modern, fast-moving, proactive Data Team that creates measurable business impact.

You may be a great fit for this role if you:

• Are not satisfied with a Data Team that only builds dashboards and reports on request.
• Want to use AI to reduce manual work and accelerate analysis.
• Can communicate with business stakeholders using business language, not just SQL.
• Can build reliable metric systems and data foundations.
• Can lead teams through workflow transformation and change.
• Care more about business outcomes than the number of reports delivered.
• Want to build a Data Intelligence Team from the existing foundation.

BENEFITS

• Competitive monthly NET salary, transparent and fully take-home
• up to 16 months’ salary per year, including a 13th-month salary, quarterly incentives, and annual performance bonuses.

• 24 remote working days per year, enabling a healthy work–life balance
• 12 days of paid annual leave, in addition to public holidays
• Flexible working hours, Monday to Friday – weekends are fully yours

• Annual health check-ups
• Full social insurance coverage (BHXH) in compliance with Vietnamese labor regulations
• Company-sponsored sports clubs to support both physical and mental well-being
• Regular company trips and team bonding activities

• Be part of a fast-growing global B2B SaaS organization
• Clear and accelerated career development and promotion pathways
• Collaborate with talented, diverse, and high-performing teams across regions
• Work in a modern, open, and empowering environment where individuality is respected and potential is nurtured