About this Data Analyst role at Weekday AI
๐ง๐ต๐ถ๐ ๐ฟ๐ผ๐น๐ฒ ๐ถ๐ ๐ณ๐ผ๐ฟ ๐ผ๐ป๐ฒ ๐ผ๐ณ ๐๐ต๐ฒ ๐ช๐ฒ๐ฒ๐ธ๐ฑ๐ฎ๐'๐ ๐ฐ๐น๐ถ๐ฒ๐ป๐๐
๐ฆ๐ฎ๐น๐ฎ๐ฟ๐ ๐ฟ๐ฎ๐ป๐ด๐ฒ: ๐ฅ๐ ๐ญ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ - ๐ฅ๐ ๐ญ๐ฑ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ (๐ถ๐ฒ ๐๐ก๐ฅ ๐ญ๐ฌ-๐ญ๐ฑ ๐๐ฃ๐)
Experience: 2+ yrs
Location: Mumbai, Maharashtra, India
Job Type: Full-time
We are looking for an experiencedย Data Analyst (BI)ย with strong hands-on expertise inย Tableau and SQLย to transform complex financial and operational data into meaningful business insights.
The role will focus on developing high-quality dashboards, reports, and visualisations that enable stakeholders to understand business performance, financial trends, risk indicators, and operational metrics. The ideal candidate will combine strong technical BI capabilities with the ability to understand business requirements and communicate insights clearly.
Requirements
KEY RESPONSIBILITIES
- Design, develop, and maintain executive and operational dashboards usingย Tableau Desktop and Tableau Server/Cloud.
- Build advanced Tableau solutions usingย LOD expressions, calculated fields, parameters, custom calculations, and action filters.
- Optimise Tableau workbooks, dashboards, and data sources to improve performance and rendering speed.
- Manage the complete Tableau development lifecycle from requirements and data preparation through deployment and maintenance.
- Write complex and performance-optimisedย SQL queriesย to extract, transform, and structure data for BI reporting.
- Prepare, cleanse, transform, and validate data for reporting and visualisation.
- Develop interactive reports and data visualisations using Tableau and related BI technologies.
- Collaborate withย Finance and Risk stakeholdersย to understand business requirements and define meaningful KPIs.
- Analyse financial and business areas includingย risk scoring, portfolio health, revenue forecasting, and operational performance.
- Create clear, story-driven visualisations that communicate complex financial trends, risks, and business insights.
- Manage data connections and publish governed data sources throughย Tableau Server/Cloud.
- Configure appropriate security, user access, permissions, and data-source settings.
- Ensure data accuracy, integrity, consistency, and governance across BI assets.
- Troubleshoot dashboard, data, performance, and connectivity issues and implement effective solutions.
- Document dashboards, data sources, business logic, and reporting processes.
- Collaborate with technical and business stakeholders to continuously improve BI capabilities and reporting quality.
WHAT MAKES YOU A GREAT FIT
- 2+ years of professional experienceย in data analytics, business intelligence, reporting, or a related field.
- 3+ years of dedicated Tableau development experienceย is preferred.
- Strong hands-on expertise withย Tableau Desktop, Tableau Server/Cloud, and Tableau Prep.
- Proven experience building complex, scalable, and production-readyย Tableau dashboards.
- Expert-levelย SQLย skills, including complex queries, joins, aggregations, transformations, and query optimisation.
- Strong understanding of data preparation, modelling, visualisation, and BI development processes.
- Experience translating business requirements into accurate, intuitive, and actionable dashboards.
- Experience working withย Finance, Risk, FinTech, Banking, or Financial Servicesย data is highly desirable.
- Strong understanding of financial and operational KPIs, data trends, and analytical reporting.
- Excellent data visualisation and storytelling skills with a strong eye for clarity and usability.
- Strong stakeholder-management and communication skills, with the ability to work effectively with business and technical teams.
- Experience with Tableau performance optimisation, data-source management, security, and BI governance.
- Tableau certification, such as Tableau Certified Data Analyst or Tableau Desktop Specialist, is an advantage.
- Working knowledge ofย Python or Rย for statistical analysis and integrating analytical outputs into BI solutions is a plus.
- Strong analytical, problem-solving, attention-to-detail, and data-quality mindset.