Business Insights & Analytics Manager

Trends Group Inc.; Philippines; On-site; PHP 1,674,000 - 3,906,000; Full time

Job summary

Trends Group Inc. seeks a Business Insights and Analytics Manager to establish and lead the organization’s analytics capability, turning multi-source business data into actionable insights, predictive models, executive dashboards, and decision-support outputs.

The role partners with business and technology stakeholders to translate questions into scalable analytics solutions, champion AI adoption, and ensure data products are accurate, secure, and well-documented to support management

Qualifications

• Bachelor’s degree in a relevant field.
• Minimum 6–8 years in data analytics, BI, or data science roles.
• 2–3 years in a supervisory/leadership role.
• Experience in B2B/B2C corporate environments and marketing funnels.

Responsibilities

• Define and lead the data analytics roadmap aligned with business goals.
• Oversee data architecture, pipelines, and BI tools for scalable analytics.
• Translate business needs into actionable analytics and present insights to leadership.
• Prioritize analytics deliverables to ensure timely, high-quality output.
• Establish data quality, privacy, and governance standards across analytics.

Skills

Analytical thinking

SQL querying

BI & dashboards

Data storytelling

Stakeholder management

Education

Bachelor’s degree in Computer Science, Data Analytics, Business, Statistics

Tools

Google Analytics

SQL

Power BI

Tableau

Snowflake

Databricks

BigQuery

Python

R

Machine Learning

Job description

Business Insights and Analytics Manager exists to establish and lead the organization’s enterprise analytics capability by converting trusted, multi-source business data into actionable insights, predictive models, executive dashboards, and decision-support outputs. The role provides strategic direction for analytics across revenue and sales performance, growth and marketing effectiveness, client technical support and lifecycle management, internal business operations, and enterprise data governance.

This position leads and develops the analytics team, partners with business and technology stakeholders to translate business questions into scalable analytics solutions, and ensures that data products are accurate, secure, well-documented, and aligned with privacy, quality, and governance standards. It also champions the adoption of AI, machine learning, self-service analytics, and data operations practices to improve business performance, operational efficiency, customer retention, and management decision-making.

II. DUTIES AND RESPONSIBILITIES

• Strategy & Leadership : Define and drive the data analytics roadmap in alignment with business goals, leading and mentoring the analytics team.
• Technical Oversight — Oversee data architecture, pipelines, and BI tools to ensure scalable, accurate, and reliable analytics infrastructure.
• Business Partnership — Translate business needs into actionable analytics solutions and present data-driven insights to leadership.
• Project & Delivery Management — Prioritize and manage analytics deliverables to ensure timely, high-quality output across stakeholders.
• Data Governance & Quality — Establish and enforce data quality, documentation, and privacy compliance standards across all analytics processes.
• People & Process — Build team capability through hiring, coaching, and continuous process improvement toward self-service analytics.
• Emerging Focus Areas — Champion AI/ML-driven and real-time analytics adoption to keep the organization ahead of evolving data trends.
• B2B Pipeline & Velocity Tracking: Design and maintain analytical models to track sales pipeline health, win/loss ratios, deal velocity, and sales representative performance.
• Revenue Forecasting: Partner with stakeholders to build data-driven revenue projection models and identify opportunities for cross-selling and up-selling within existing enterprise accounts.
• Pricing & Margin Analysis: Analyze B2B pricing structures, discount impacts, and contract profitability to optimize commercial margins (including ARPA).
• Growth & Marketing Analytics
• Marketing Performance & Attribution Analytics: Oversee measurement of campaigns, ABM, funnel/attribution models, and digital/CRM/marketing automation performance and provide actionable insights and recommendations to improve pipeline contribution, conversion, and revenue outcomes.
• Digital & Reputation Intelligence: Oversee digital performance, content effectiveness, brand sentiment, media monitoring, social listening, and competitive share-of-voice analysis to support engagement, reputation management, and crisis preparedness.
• Market & Customer Intelligence: Lead market research, customer insight, and competitive intelligence initiatives to identify growth opportunities and deliver strategic recommendations, forecasts, and business intelligence outputs that support business decision‑making.
• Client Technical Support & Lifecycle Management
• Support Operations & SLAs: Analyze service ticket trends, resolution times (MTTR), and support bottlenecks to optimize client technical support efficiency.
• Renewals & Warranty Analytics: Build proactive models tracking contract expiration, maintenance agreements, hardware/software warranty lifecycles, and software renewals.
• Churn & Customer Health Metrics: Develop "Customer Health Scores" by combining product usage data, support ticket history, and engagement metrics to predict and mitigate client churn.
• Internal Business Operations & Strategy
• Operational Efficiency Tracking: Identify operational bottlenecks, resource utilization rates, and process inefficiencies across corporate business units.
• Executive Reporting & Dashboards: Lead the design, development, and maintenance of standardized automated enterprise dashboards (e.g., Power BI, Tableau) for Senior Management and C‑Suite review.
• Ad-Hoc Strategic Discovery: Conduct deep‑dive, one‑time exploratory data analyses to answer critical operational questions from business leaders (e.g., "What factors drive our peak support volume?").
• Data Governance & Team Leadership
• Data Consumption Standards: Ensure the Analytics team consumes data responsibly, strictly utilizing standardized "Gold" data models and maintaining data privacy and security compliance in coordination with governance, risk and compliance teams.
• People Management: Lead, mentor, and upskill a high‑performing team of data analysts and BI professionals, fostering a data‑driven culture.
• Pipeline Collaboration: Partner closely with the Data Engineering & Architecture team to articulate business data requirements, ensuring the data systems are ingesting and cleaning the appropriate source fields required for reporting.
• AI Analytics and Data Operations
• Champion the adoption of machine learning and AI‑driven analytics to enable predictive insights and advanced decision support.
• Design, build and deploy AI agents in support of the organization’s drive towards improvements on both efficiency and capability.
• Provide data management and operations support as maybe required for both internal and external clients.

III. QUALIFICATIONS

• Bachelor’s degree in Computer Science, Data Analytics, Business, Statistics, or a related field.
• Minimum of 6–8 years of experience in data analytics, business intelligence, or data science roles.
• At least 2–3 years in a supervisory or team leadership capacity.
• Experience working within a B2B/B2C corporate environment, understanding enterprise sales cycles, contract‑based client lifecycles, and various marketing funnels.

C. Competency

Technical Skills:

• Tools related to web analytics (e.g. Google Analytics)
• Advanced proficiency in SQL for data extraction and manipulation.
• Expertise in BI and data visualization platforms (e.g., Power BI, Tableau).
• Strong familiarity with modern data warehousing environments (e.g., Snowflake, Databricks, Big Query) and data architecture concepts.
• Familiarity with scripting languages for analytics (Python or R) and basic statistical modeling/predictive analytics.
• Familiarity with machine learning fundamentals (regression/classification/clustering), AI use cases and tools, including agentic AI development

Soft Skills:

• Exceptional communication and storytelling skills—the ability to translate complex data matrices into clear, plain-English strategic takeaways for non‑technical executives.
• Strong stakeholder management skills with a consultative approach to problem‑solving.
• High business acumen and curiosity to understand the underlying drivers of a business.

IV. WORKING CONDITIONS

• May require working beyond normal business hours, including weekends and holidays, to meet project deadlines or address urgent analysis requirements.