Senior Executive - Data & Analytics

Job description:

Job Summary

The Data & Analyst Engineer is responsible for developing, maintaining, and improving the organisation's data pipelines, data models, data marts, and analytics infrastructure. The role
supports business reporting, dashboarding, predictive analytics, prescriptive analytics, and AI readiness by ensuring that data is accurate, well-structured, reliable, and accessible across departments.

Key Responsibilities

1. Data Pipeline Development
   Design, build, and maintain data pipelines using Python and Airflow.
   Automate data ingestion, transformation, validation, and loading processes.
   Integrate data from internal systems, external sources, spreadsheets, APIs, and databases into Google BigQuery.
2. BigQuery and SQL Development
   Develop, optimise, and maintain SQL scripts, views, stored procedures, and data transformation logic in Google BigQuery.
   Ensure data quality, consistency, performance, and scalability across reporting and analytics layers.
   Troubleshoot data discrepancies, refresh failures, and pipeline issues.
   3. Data Modelling and Data Mart Creation
   Design and maintain structured data models to support business reporting and analytics.
   Build departmental data marts for Finance, Sales & Marketing, HR, PMU, MI/BDU, CnC, and other business functions.
   Standardise business metrics, definitions, dimensions, and calculation logic across dashboards and reports.
   4. Predictive and Prescriptive Analytics
   Support predictive analytics use cases such as sales forecasting, revenue forecasting, customer segmentation, sales funnel analysis, inventory movement prediction, risk identification, and campaign performance analysis.
   Contribute to prescriptive analytics initiatives such as scenario analysis, optimisation, recommendation-driven insights, and decision-support models.
   Translate business problems into analytical use cases, data requirements, and measurable outcomes.
   Prepare clean, structured, and reusable datasets to support machine learning, forecasting, and AI-driven analytics.
   5. Data Architecture and Framework

Support the development of the organisation's data architecture framework,
including staging, transformation, semantic, analytics, and reporting layers.
Apply best practices in data governance, lineage, security, access control, and documentation.
Contribute to scalable data warehouse design to support BI, predictive analytics, prescriptive analytics, and AI use cases.

6. Analytics and Dashboard Support
Support dashboard maintenance, enhancement, and new dashboard initiatives.
Work with business users to understand reporting requirements, translate them into data models, and deliver reliable datasets.
Perform data analysis to identify trends, risks, opportunities, and business insights.

7. Data Quality and Documentation
Implement data validation checks, reconciliation logic, and exception handling.
Maintain documentation for data pipelines, data marts, business rules, metric
definitions, and data flow processes.
Ensure datasets are audit-ready, traceable, and aligned with business
requirements.

Qualifications

• Bachelor's degree in Computer Science, Information Technology, Statistics, Mathematics, Business Analytics, Economics, or a related quantitative field.

• Proven experience in Business Intelligence development or data analysis.

• Strong proficiency in SQL for data extraction and manipulation.

• Hands-on experience with at least one major BI visualization tool (e.g., Tableau, Power BI, Qlik Sense/View).

Skills & Abilities

Required Skills and Experience
Strong SQL skills, especially in data transformation, joins, aggregations, window functions, and query optimisation.
Hands-on experience with Google BigQuery or similar cloud data warehouse platforms.
Experience using Python for data pipeline development, automation, and data processing.
Familiarity with Airflow or other workflow orchestration tools.
Understanding of data modelling concepts, including fact tables, dimension tables, star schema, and data mart design.
Experience in ETL/ELT pipeline creation and data architecture design.
Ability to analyse business requirements and translate them into technical data solutions.
Understanding of predictive analytics, forecasting, segmentation, scenario analysis, and optimisation.
Strong problem-solving skills and attention to detail.
Experience with Power BI, dashboard datasets, or semantic models.
Knowledge of data governance, data quality frameworks, and access control.
Experience integrating data from APIs, spreadsheets, ERP systems, CRM systems, or external data sources.
Exposure to machine learning, predictive modelling, optimisation, or AI-related data preparation.
Experience working with finance, sales, marketing, property, HR, project, or customer-related business data.

Expected Minimum Years of Experience

3 Years and above in relevant working experience