BI Developer | Q-Commerce

We are looking for a BI Developer who can design, develop, and maintain business intelligence solutions while also taking ownership of the underlying data warehouse, data models, and analytical data pipelines.

The ideal candidate has strong experience in SQL, Power BI, DAX, data warehousing, dimensional modeling, Airflow, and database technologies, and understands how different database systems should be used depending on the workload and business requirements.

This role works closely with Data Engineers, Data Scientists, Product, Operations, Finance, and other business teams to build reliable and scalable data solutions.

Key Responsibilities

1. Business Intelligence & Reporting

• Design, develop, and maintain dashboards and reports using Power BI.
• Develop and maintain DAX measures and analytical calculations.
• Build and maintain SSAS Tabular / semantic models.
• Translate business requirements into scalable analytical solutions.
• Define, standardize, and maintain business KPIs and metrics.
• Optimize reports and semantic models for performance and usability.

2. Data Warehouse Development & Maintenance

• Develop, maintain, and optimize the company's Data Warehouse.
• Design and implement fact and dimension tables.
• Develop and maintain Star Schema / dimensional models.
• Design appropriate data structures for analytical workloads.
• Develop SQL views, stored procedures, transformations, and analytical datasets.
• Monitor data warehouse performance, data quality, and storage.
• Identify and resolve data inconsistencies and data quality issues.
• Continuously improve the warehouse architecture based on business and technical requirements.

3. Data Pipeline & Airflow

• Develop and maintain ETL/ELT pipelines using Apache Airflow.
• Create and maintain DAGs for data ingestion and transformation.
• Monitor pipeline execution and troubleshoot failed jobs.
• Implement appropriate retry, dependency, scheduling, and alerting mechanisms.
• Ensure data pipelines are reliable, observable, and maintainable.
• Work with Data Engineers to improve pipeline architecture and data availability.
• Develop incremental and scalable data loading strategies.

4. Database Technologies

The candidate should have a strong understanding of different database technologies and their appropriate use cases. The candidate should understand the differences between:

• OLTP vs OLAP databases
• Relational vs NoSQL databases
• Row-oriented vs column-oriented databases
• Transactional vs analytical workloads
• SQL Server / PostgreSQL / MySQL
• Analytical databases such as ClickHouse
• Data warehouses and data lakes
• Different indexing, partitioning, and storage strategies

They should be able to evaluate a database based on factors such as:

• Query patterns
• Data volume
• Read/write workload
• Transaction requirements
• Analytical requirements
• Scalability
• Performance
• Consistency
• Storage architecture

5. Data Modeling

• Design scalable and maintainable data models.
• Apply dimensional modeling principles.
• Design fact tables, dimension tables, and aggregate tables.
• Understand Star Schema and Snowflake Schema.
• Understand different types of dimensions and slowly changing dimensions.
• Design models appropriate for both BI and analytical workloads.
• Understand normalization and denormalization and when each should be used.
• Optimize data models for Power BI, SSAS, SQL Server, and analytical databases.

6. SQL & Performance Optimization

• Write complex and optimized SQL/T-SQL queries.
• Analyze query execution plans and identify performance bottlenecks.
• Optimize joins, indexes, partitions, aggregations, and data structures.
• Investigate slow-running queries and inefficient data models.
• Work with database engineers when deeper database optimization is required.

7. Data Quality & Reliability

• Ensure data is accurate, complete, and consistent.
• Implement data validation and quality checks within pipelines.
• Monitor warehouse and pipeline failures.
• Investigate discrepancies between different data sources and reports.
• Ensure BI reports are based on trusted and well-defined datasets.

8. Cross-Team Collaboration

• Work closely with Data Engineers to design and improve the data platform.
• Work with Data Scientists to provide reliable analytical datasets.
• Work with business stakeholders to understand requirements and define KPIs.
• Communicate technical concepts clearly to non-technical stakeholders.
• Take ownership of BI and analytical solutions from requirement gathering through deployment and maintenance.

Required Technical Skills

Must Have

• Strong SQL / T-SQL
• Strong Power BI
• Strong DAX
• Experience with SSAS Tabular / Semantic Models
• Strong understanding of Data Warehousing
• Strong understanding of Dimensional Data Modeling
• Experience with Apache Airflow
• Experience developing and maintaining ETL/ELT pipelines
• Strong understanding of database architecture and different database types
• Experience with relational databases such as SQL Server, PostgreSQL, or MySQL
• Understanding of OLTP and OLAP architectures
• Understanding of Star Schema, Fact and Dimension modeling
• Experience with query and data-model performance optimization

Nice to Have

• Experience with ClickHouse
• Experience with Kafka / streaming data
• Experience with Data Lakes and object storage
• Experience with Docker
• Experience with Git and CI/CD
• Experience with Kubernetes
• Experience in e-commerce, retail, marketplace, logistics, or q-commerce
• Experience with large-scale data warehouses