We are looking for a highly skilled, hands-on Data Analyst / BI Developer who is exceptional in Tableau and even stronger in advanced SQL and data understanding. The role will own the complete analytics lifecycle—from understanding a business question and validating source data to building optimized SQL datasets and publishing reliable, decision-ready Tableau dashboards.

The successful candidate must be able to work independently with large, complex datasets, identify data-quality issues, explain the business meaning behind numbers, and convert analysis into transparent, actionable insights. Strong Python development capability is required. DBMS experience and/or React knowledge will be an added advantage.

KEY RESPONSIBILITIES

- Partner with business and functional teams to understand processes, KPIs, reporting requirements, decision points, and data definitions.
- Design, develop, publish, and maintain enterprise-grade Tableau dashboards, reports, and reusable data sources.
- Build efficient and reliable SQL queries for complex analysis, reconciliation, reporting, and data preparation.
- Investigate data discrepancies by tracing data from source systems through transformations to final dashboards.
- Define data grain, joins, business rules, metric logic, filters, and exception-handling rules before visualization.
- Optimize Tableau workbooks, extracts, live connections, calculations, and underlying SQL for performance and scalability.
- Develop Python-based utilities and workflows for data extraction, transformation, validation, automation, API integration, and recurring analysis.
- Perform exploratory analysis, identify trends and anomalies, and communicate findings in simple business language.
- Create documentation covering metric definitions, data lineage, dashboard logic, refresh schedules, assumptions, and known limitations.
- Apply appropriate access controls, row-level security, testing, version control, and deployment practices.
- Support users after release, troubleshoot production issues,



and continuously improve dashboards based on usage and feedback.
- Coach stakeholders and team members on effective dashboard use and sound interpretation of data.

MANDATORY TECHNICAL SKILLS

1. Exceptional Tableau expertise

• Deep hands-on knowledge of Tableau Desktop and Tableau Server/Cloud.
- Expert-level capability in calculated fields, Level of Detail (LOD) expressions, table calculations, parameters, sets, context filters, dashboard actions, data blending/relationships, and reusable data sources.
- Strong understanding of extracts versus live connections, refresh strategy, row-level security, publishing, permissions, subscriptions, and dashboard governance.
- Proven ability to create clean, intuitive, executive-ready dashboards without compromising analytical depth.
- Strong Tableau performance-tuning skills, including reducing query load, optimizing calculations, improving workbook design, and using performance diagnostics.

1. Expert-level advanced SQL

• Excellent command of complex joins, CTEs, subqueries, window functions, conditional aggregation, set operations, pivots/unpivots, date logic, and advanced analytical queries.
- Ability to write accurate SQL for large datasets while handling duplicates, nulls, changing master data, incomplete records, and different levels of granularity.
- Strong understanding of query execution plans, indexing, partitioning, views/materialized views, and performance optimization.
- Ability to independently reconcile results across source tables and prove the correctness of reported numbers.
- Sound knowledge of relational database concepts, normalization, dimensional modelling,



fact/dimension design, and data marts.

1. Strong data and business understanding

• Ability to translate an unclear business requirement into precise questions, definitions, source mappings, and acceptance criteria.
- Strong understanding of data quality, data lineage, master data, transaction data, historical versus current-state reporting, and source-of-truth controls.
- Ability to distinguish correlation from causation, validate assumptions, identify anomalies, and explain why a metric changed—not only what changed.

1. Python development

• Strong hands-on Python skills for data processing and automation.
- Experience with pandas, NumPy, database connectivity, REST APIs, file processing, logging, exception handling, and reusable modular code.
- Ability to build and maintain ETL/ELT utilities, validation scripts, scheduled workflows, and analytical prototypes.
- Familiarity with Git, code review, testing, and maintainable development practices.

PREFERRED / ADDED ADVANTAGE

- DBMS expertise, including schema design, indexing, EXPLAIN/EXPLAIN ANALYZE, functions, views, materialized views, and database performance tuning.
- React knowledge for building lightweight analytical applications, integrating dashboards, or creating custom data-driven user interfaces.
- Exposure to data warehouses, cloud data platforms, orchestration tools, APIs, and modern data pipelines.
- Experience working with operational, sales, finance, supply-chain, or manufacturing data will be useful.

QUALIFICATIONS AND EXPERIENCE

- Bachelor’s or Master’s degree in Computer Science, Information Technology, Data Science, Statistics, Engineering, or a related discipline.
- Demonstrable experience delivering production Tableau solutions and writing complex SQL in a business environment.
- A strong portfolio or practical examples of dashboards, SQL problem-solving, performance tuning, and data reconciliation will be preferred.

📌 Data Analyst (India)
🏢 Dharampal Satyapal Group (DS Group)
📍 India