Data Analyst - Digital Business & Financial Analytics

• Extensive expertise in developing advanced SQL queries, including aggregate functions, CTEs, window functions, complex joins, and query optimization across relational databases such as MS SQL Server, Oracle, PostgreSQL, or equivalent platforms.

• Practical experience with scripting languages such as Python, R, or Shell, along with libraries like pandas and BeautifulSoup for automation, data processing, cleaning, and web scraping.

• Strong command of enterprise Business Intelligence tools and expression languages, including Power BI, DAX, Power Query M, Metabase, Tableau, and Looker.

• Comfortable working with semi-structured datasets, cloud-based spreadsheets, and integrated storage platforms such as Power Query, Excel, SharePoint, and Google Sheets.

• Demonstrated ability to quickly adapt to and learn emerging tools, technologies, and frameworks according to evolving business requirements.

• Self-motivated and capable of transforming unstructured business requirements into complete data pipelines, analytical models, and actionable outputs independently.

• Strong interest or practical knowledge of stock brokerage, equity markets, mutual funds, and asset management operations.

• Able to work effectively with multiple cross-functional leaders and manage diverse stakeholder expectations with professionalism and clarity.

Responsibilities & Context

1. Source Preparation, Staging & Pipeline Automation

• Design, develop, and maintain reliable ELT pipelines to extract, transform, and load data from financial systems, core trading databases, web APIs, and other external sources.

• Develop and monitor automated data extraction, web scraping, and file-processing scripts using Python, Shell scripting, or similar technologies.

• Maintain efficient staging environments, optimize database schemas, and manage automated data refresh workflows across reporting platforms.

2. Data Modeling, Visualization & Predictive Analytics

• Develop scalable and well-structured relational data models, including star and snowflake schemas, to establish a reliable single source of truth across securities and asset management operations.

• Create intuitive, executive-level dashboards and operational reports using Business Intelligence tools such as Power BI, Metabase, Tableau, or equivalent platforms.

• Develop scenario analysis models, forecasts, performance simulations, and predictive metrics to support strategic and data-driven decision-making.

3. Cross-Entity Stakeholder Engagement & Data Governance

• Serve as a centralized data partner across multiple business units while coordinating with stakeholders from different areas of the organization.

• Translate complex financial data, trends, and analytical findings into clear and actionable insights for non-technical management and leadership teams.

• Maintain high standards of data accuracy, integrity, security, and governance across all data operations.

• Prepare and maintain comprehensive documentation covering data pipelines, data dictionaries, business rules, and analytical logic to ensure long-term scalability and consistency.