Key Accountabilities
We are seeking an experienced Data Analytics Manager with a strong Cloud-First mindset to lead our analytics initiatives. The ideal candidate will bridge the gap between technical data engineering and business strategy. You will be responsible for overseeing the end-to-end data lifecycle—from data ingestion and warehousing in Google BigQuery to the visualization and delivery of actionable insights using Looker.
You will lead a team of analysts and engineers to build robust data pipelines and scalable dashboards that support key clients (including public sector and enterprise projects). You will play a pivotal role in driving data maturity within the organization, ensuring data accuracy, governance, and accessibility for decision-making.
Job Summary
Key Accountabilities:
A. Data Strategy & Architecture (Google Cloud Platform)
- Design and oversee the implementation of scalable data warehousing solutions using BigQuery. Ensure optimal schema design, partitioning, and clustering for performance and cost efficiency.
- Define the roadmap for data maturity, moving the organization from descriptive analytics to predictive and prescriptive capabilities.
- Perform data extraction, transformation, and loading of large and complex data sets from various source systems.
- Ensure data quality and integrity by implementing data validation and testing within ETL processes.
- Establish data governance standards, ensuring data integrity, security (IAM roles), and compliance with local regulations (PDPA).
B. Advanced Analytics & Visualization (Looker)
- Serve as the subject matter expert for Looker and LookML. Architect semantic data models that allow non-technical users to explore data self-sufficiently.
- Oversee the creation of high-impact executive dashboards that visualize complex datasets simply and effectively.
- Troubleshoot and optimize slow-performing queries and dashboard rendering times.
- Stay informed on the latest trends in data management, analytics, and AI technologies, and provide insights on how these innovations can enhance business outcomes.
C. Team Leadership & Management
- Manage, mentor, and develop a team of Data Analysts and Data Engineers.
- Conduct code reviews (SQL/LookML) to ensure best practices are followed.
- Manage resource allocation for multiple concurrent projects, ensuring deadlies are met for critical deliverables.
- Contribute to preparing presentations, demonstrations, and proof-of-concepts to showcase the effectiveness and potential of our analytics solutions.
- Assist in developing proposals and responses to RFPs/RFQs, ensuring alignment with client needs and analytics capabilities.
D. Stakeholder Management
- Translate complex data findings into clear business narratives for senior management and external clients.
- Collaborate with the Software Development and Cloud Infrastructure teams to ensure seamless data integration across applications.
Qualification
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Information Technology, or a related field.
Desired experience/exposure
- Minimum 8+ years of progressive experience in data analytics, business intelligence, or data engineering.
- At least 3 years in a managerial or lead capacity, overseeing technical teams.
- Proven track record of delivering enterprise-scale analytics projects.
Competencies
Special skills required
- Google BigQuery: Expert-level proficiency in SQL, including window functions, nested data structures, and query optimization within a BigQuery environment.
- Looker: Extensive experience in developing LookML models, Explores, and user-facing dashboards. (Experience with Looker Studio alone is insufficient; full Looker platform experience is required).
- Data Pipeline Tools: Familiarity with ETL/ELT tools (e.g., dbt, Airflow, or Dataflow).
- Cloud Ecosystem: Solid understanding of the wider Google Cloud Platform (GCP) ecosystem (Cloud Storage, Cloud Functions, IAM)
Personal attributes
Technical Competencies
- Data Modeling: Strong grasp of Dimensional Modeling (Star/Snowflake schemas) and Denormalization techniques for Big Data.
- Statistical Analysis: Ability to apply statistical methods to validate findings.
- Performance Tuning: Ability to diagnose bottlenecks in data processing and visualization layers.
Behavioral Competencies
- Strategic Thinking: Ability to see the big picture and align data projects with Awantec's business goals (e.g., Talent & Technology Digitalization).
- Communication & Storytelling: Exceptional ability to communicate technical concepts to non-technical stakeholders.
- Adaptability: Comfortable working in a fast-paced environment with shifting priorities.
- Problem Solving: A proactive approach to identifying data gaps and proposing innovative solutions.
