• Requirements Gathering & Solution Design: Partner closely with business departments and system integration project teams to elicit, analyze, and clarify business and data requirements; propose effective data exploitation and integration solutions to drive business value.
• Data
Product Ownership: Act as the main Product Owner for end-to-end data initiatives, taking full accountability for the implementation, maintenance, and continuous optimization of data products.
• Bridge Business & Data Architecture: Serve as the Data BA in cross-functional projects, seamlessly translating business requirements and domain logic into technical data warehouse requirements.
• Ad-hoc Data & Reporting Support: Streamline and fulfill ad-hoc data requests and reports for various departments and project teams (minimal frequency as routine reporting is already handled by the core BI team).
• Data Quality & Documentation Management: Maintain up-to-date data dictionaries, business process and data mapping documentation, and conduct thorough validation/testing to ensure high data quality and integrity.
• Data Governance Compliance: Maintain comprehensive requirement specifications and ensure all data processes strictly adhere to the company's overall data governance policies.
• Ability to communicate with non-technical/ technical peers.
• Minimum 4-5+ years of experience as a Data
Business Analyst, Analytics Engineer, Data Analyst, or in a similar role with a proven track record of bridging the gap between business and technology.
• Strong experience working with Data Warehouse (DWH) systems, understanding concepts of data warehousing, data marts, ETL/ELT processes, and star/snowflake schemas.
• Proven experience in Data Governance practices, including building and maintaining data dictionaries, data lineage, and ensuring data quality control.
• Prior experience in the F&B, Retail, or E-commerce sectors is highly preferred (handling data from POS, CRM, ERP, loyalty apps, etc.).
• Advanced SQL proficiency: Ability to write complex, optimized queries to analyze, validate, and troubleshoot data issues directly in the database.
• Strong understanding of Data Modeling concepts and the ability to map business logic into data structures.
• Familiarity with modern data tools and stack (e.g., dbt, Airflow, or cloud Warehouses like Redshift/BigQuery/ClickHouse) is a plus.
• Solid command of Data Visualization tools (Power BI, Tableau, or Superset) to understand how data is presented, though routine report creation is minimal.
• Basic knowledge of Python or R for data manipulation/automation is a strong plus.