Data Engineer at Weekday AI

Mumbai, maharashtra, India -

Full Time

Start Date

Immediate

Expiry Date

07 Jun, 26

Salary

0.0

Posted On

09 Mar, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Sql, Power Bi, Microsoft Fabric, Azure Data Platforms, Lakehouse Architecture, Data Modeling, Etl/Elt, Data Warehousing, Data Pipelines, Data Quality, Performance Optimization

Industry

technology;Information and Internet

Description

This role is for one of the Weekday's clients Min Experience: 2 years Location: Mumbai JobType: full-time We are seeking a skilled and detail-oriented Data Engineer (Sales Analytics) to join a leading jewellery brand based in Mumbai. This role is ideal for a professional who enjoys working with data, building scalable pipelines, and transforming complex datasets into meaningful insights that drive business performance. As a Data Engineer, you will play a key role in supporting the sales and business intelligence teams by designing reliable data systems and enabling data-driven decision-making across the organization. In this position, you will work closely with sales, analytics, and business teams to ensure that data from multiple sources is collected, processed, and delivered in a structured format suitable for reporting and advanced analytics. You will help build and maintain robust data pipelines and data models that power dashboards, reports, and insights used by leadership and operational teams. Key Responsibilities Design, develop, and maintain scalable data pipelines to process and transform large volumes of sales and operational data. Build and optimize SQL-based data models to support reporting, analytics, and business intelligence initiatives. Develop and maintain interactive dashboards and reports using Power BI to enable stakeholders to monitor sales performance and key metrics. Work with Azure Data Platform services and Microsoft Fabric to manage data ingestion, transformation, and storage. Implement and manage Lakehouse architecture for structured and semi-structured data, ensuring efficient data storage and retrieval. Collaborate with sales, finance, and operations teams to understand data requirements and translate them into scalable data solutions. Ensure data quality, integrity, and consistency across various data sources and reporting platforms. Optimize data workflows for performance, scalability, and reliability. Troubleshoot data pipeline issues and implement improvements to enhance system efficiency. Document data architecture, pipelines, and workflows to support maintainability and collaboration. Skills & Technical Expertise We are looking only for candidates who demonstrate strong proficiency in the following areas: SQL: Advanced knowledge of writing optimized queries, data transformation, and database management. Power BI: Experience in creating dashboards, data models, and visual analytics for business stakeholders. Microsoft Fabric: Hands-on exposure to Fabric’s data engineering capabilities and integrated analytics environment. Azure Data Platforms: Experience working with Azure-based data services for storage, processing, and analytics. Lakehouse Architecture: Understanding of modern data lakehouse concepts, including data organization, ingestion, and governance. Experience & Qualifications 2–4 years of professional experience in data engineering, data analytics, or business intelligence roles. Experience working with large datasets and building data pipelines in cloud environments. Strong understanding of data modeling, ETL/ELT processes, and data warehousing concepts. Ability to translate business requirements into technical data solutions. Strong analytical thinking and problem-solving abilities. Excellent communication skills and ability to collaborate with cross-functional teams.

Responsibilities

The Data Engineer will design, develop, and maintain scalable data pipelines to process and transform large volumes of sales and operational data, while building and optimizing SQL-based data models to support reporting and business intelligence.