Data Engineer

Innovate in Bengaluru

This position is based at our on-site office in Bengaluru. Lowe's offers an ultramodern work environment, complete with cutting-edge technology, collaborative workspaces, an on-site gym and clinic, and other perks to enhance your work experience.

About Lowe’s

Lowe’s is a FORTUNE® 100 home improvement company serving approximately 16 million customer transactions a week in the United States. With total fiscal year 2024 sales of more than $83 billion, Lowe’s operates over 1,700 home improvement stores and employs approximately 300,000 associates. Based in Mooresville, N.C., Lowe’s supports the communities it serves through programs focused on creating safe, affordable housing, improving community spaces, helping to develop the next generation of skilled trade experts and providing disaster relief to communities in need. For more information, visit Lowes.com

Lowe’s India, the Global Capability Center of Lowe’s Companies Inc., is a hub for driving our technology, business, analytics, and shared services strategy. Based in Bengaluru with over 4,500 associates, it powers innovations across omnichannel retail, AI/ML, enterprise architecture, supply chain, and customer experience. From supporting and launching homegrown solutions to fostering innovation through its Catalyze platform, Lowe’s India plays a pivotal role in transforming home improvement retail while upholding strong commitment to social impact and sustainability. For more information, visit Lowes India

About the Team

Our team focuses on understanding how inventory moves through the store ecosystem—from replenishment and on-hand accuracy to sell-through, stockouts, overstocks, and fulfillment readiness. We build scalable analytics, reporting, and decision-support solutions that help business teams identify opportunities, prioritize actions, and measure outcomes.

The team combines strong business knowledge with advanced analytics to answer critical questions such as

• Are customers able to find the products they need in store?
• Where are inventory gaps, excesses, or accuracy issues affecting performance
• Which items, categories, or locations require attention?
• How can inventory decisions improve sales, customer experience, and productivity

By delivering trusted data products and clear, actionable insights, Store Inventory Analytics helps ensure the right products are available in the right stores at the right time.

Job Summary

executing direction from leadership, delivering results that align with strategic objectives, communicating critical information to other teams, managing vendor relationships, developing processes that align to organizational goals, specific technical skills required for managing a process.

The primary purpose of this role is to build, maintain, and validate reusable analytical data assets that enable trusted reporting, self-service analytics, dashboards, AI-assisted insights, and business decision-making. This includes following data engineering best practices, developing accurate and scalable data logic, supporting semantic layer development, and ensuring that metrics and datasets are consistent, documented, and reusable.

This role aids in supporting one functional area of the business in partnership with Analytics, Product, Engineering, Data Engineering, and other team members. The Data engineer works with guidance from senior team members to translate business and reporting needs into governed analytical models, reusable queries, semantic layer objects, data quality checks, and self-service reporting assets.

Roles & Responsibilities

Core Responsibilities

• Assist senior managers, lead data engineers, senior data engineers with building project plans and ensuring clear understanding of business requirements, technical requirements, data dependencies, and timelines.
• Provide high-quality and timely delivery of reusable analytical assets, governed metric logic, semantic layer objects, self-service explores, and data validation outputs.
• Translate defined business questions, metric definitions, and reporting requirements into reusable analytical logic, data models, measures, dimensions, and semantic layer components.
• Utilize knowledge shared by more senior associates on how to best leverage disparate data sources, relevant internal/external data, enterprise data platforms, and domain expertise to deliver scalable analytical solutions.
• Develop and maintain reusable analytical datasets, semantic layer objects, explores, dashboards, reports, and visualizations using business intelligence, reporting, and semantic layer tools.
• Perform data validation, reconciliation, and quality checks to ensure analytical outputs are accurate, consistent, impactful, timely, and efficient.
• Document metric definitions, business logic, source-to-target mappings, transformation rules, filters, assumptions, data lineage, and known limitations.
• Support dashboard and reporting rationalization by identifying recurring requests, duplicate logic, or manual data pulls that can be converted into reusable self-service assets.
• Translate complex data structures and metric logic into understandable documentation and guidance for analysts, Product, Engineering, and business stakeholders.
• Provide input into frameworks used to measure the impact of data engineering work, including reduced manual data pulls, increased self-service adoption, improved metric consistency, improved dashboard performance, and higher trust in analytical outputs.
• Participate in the process of testing and deploying new analytical data assets, semantic layer updates, tracking solutions, and integrations while monitoring and optimizing existing assets.
• Partner with Analytics, Product, Engineering, and Data Engineering teams to identify and resolve data issues, metric discrepancies, transformation logic gaps, and reporting inconsistencies.
• Support AI-assisted analytics and Newton Analyst enablement by helping ensure approved metrics, governed datasets, and semantic definitions are accurate and reusable.
• Seek to understand common methods and best practices in data engineering, including data modeling, semantic layer development, metric governance, data quality, documentation, version control, testing, and reusable code development.

Years of Experience

• 2 years of experience in data, business intelligence, or platform engineering, data warehousing/ETL, or software engineering
• 2 years of expertise in object-oriented programming/structure programming, SQL, and scripting
• 2 years of experience in big data technology and Cloud big data technologies
• 1 year of experience working on project(s) involving the implementation of solutions applying development life cycles (SDLC)

Education Qualification & Certifications (optional)

Required Minimum Qualifications

• Bachelor’s degree in engineering, computer science, computer information systems (CIS), or related field or equivalent years of experience in lieu of education requirement, if applicable

Minimum Qualifications

• Bachelor’s degree in engineering, computer science, computer information systems (CIS), or related field or equivalent years of experience in lieu of education requirement, if applicable
• 2 years of experience in data, business intelligence, or platform engineering, data warehousing/ETL, or software engineering
• 2 years of expertise in object-oriented programming/structure programming, SQL, and scripting
• 2 years of experience in big data technology and Cloud big data technologies
• 1 year of experience working on project(s) involving the implementation of solutions applying development life cycles (SDLC)

Skill Set Required

Primary Skills (must have)

Experience working with large, complex, or unstructured data in a retail environment.

Experience working with enterprise-level databases, cloud data platforms, or data warehouses such as GCP, BigQuery, Hadoop, Teradata, Azure, Oracle, DB2, Snowflake, or similar platforms.

Experience with semantic layer, business intelligence, or self-service reporting tools such as Looker, Looker Core, Power BI, Tableau, MicroStrategy, or similar tools.

Experience with LookML, dbt, SQL-based transformation frameworks, semantic modeling tools, or similar data engineering technologies.

Exposure to version control, code review, testing frameworks, release management, documentation standards, or deployment practices.

Experience with data quality checks, metric validation, dashboard reconciliation, or change-impact analysis.

Experience supporting self-service analytics, dashboard rationalization, reporting automation, or metric governance initiatives.

Lowe’s is an equal opportunity employer and administers all personnel practices without regard to race, color, religious creed, sex, gender, age, national origin, mental or physical disability or medical condition, sexual orientation, gender identity or expression, marital status, military or veteran status, genetic information, or any other category protected under state or local law. Lowe’s wishes to maintain appropriate standards and integrity in meeting the requirements of the Information Technology Act’s privacy provisions.

Lowe's is an equal opportunity employer and administers all personnel practices without regard to race, color, religious creed, sex, gender, age, ancestry, national origin, mental or physical disability or medical condition, sexual orientation, gender identity or expression, marital status, military or veteran status, genetic information, or any other category protected under federal, state, or local law.