Data Engineer

MHPI (Masco Home Products India); India; Remote; INR 1,500,000 - 3,500,000; Full time

Job summary

Masco Home Products India (MHPI) designs, builds, and operates the ingestion, transformation, and Lakehouse data pipelines powering Masco's enterprise POS and analytics. You will deliver high-quality, secure data pipelines across ingestion, transformation, and enrichment in collaboration with the Enterprise Data Architect.

As a Data Engineer, you will own ingestion pipelines, API integrations, data quality checks, CI/CD automation, and documentation, enabling end-to-end data workflows on Azure

Qualifications

• Bachelor's or master's degree in computer science, Engineering, Data Analytics, or a related field; equivalent professional experience considered.
• Substantial hands-on experience designing and building data engineering solutions on a major cloud platform, with emphasis on the Azure ecosystem.
• Experience delivering production pipelines and supporting them in an operational setting.

Responsibilities

• Design, build, and maintain automated ingestion pipelines and multi-source integration for retailer, HQ, and BU data on the Databricks and Azure data stack.
• Develop RESTful APIs and API-based integrations for retailer and third-party data acquisition.
• Deliver critical-path pipelines and validation for priority data feeds.
• Integrate with orchestration tools and cloud or hybrid storage systems to enable end-to-end data workflows.
• Contribute to Lakehouse solutions using ACID storage layers, schema enforcement, and versioning for reliable data management.
• Run data-quality checks, implement monitoring, and support incident response and continuous improvement.
• Document ingestion patterns, pipelines, and metric definitions; contribute pipeline lineage to enterprise catalog.

Skills

Databricks

Azure data stack

SQL

Python

RESTful API

ETL/ELT

Apache Spark

Data quality

CI/CD

Data governance

Education

Bachelor's or Master's in CS/Engineering/Data Analytics

Tools

Databricks

Azure Data Factory

Data Lake

Synapse

Analysis Services

Microsoft Fabric

Job description

Masco Home Products India (MHPI) is a fully owned subsidiary of Masco Corporation, headquartered in Livonia, MI. The vision of MHPI is to be recognized as a world-class Global Business Services organization driven by the desire for excellence in its people, business solutions, execution, and partnerships with internal customers to develop “Lean and Simple” business solutions.

Headquartered in Livonia, Michigan, Masco Corporation is a global leader in the design, manufacture and distribution of branded home improvement and building products. Our portfolio of industry-leading brands includes Behr® paint; Delta® and Hansgrohe® faucets, bath and shower fixtures; Liberty® branded decorative and functional hardware; and HotSpring® spas. We leverage our powerful brands across product categories, sales channels and geographies to create value for our customers and shareholders.

For more information about Masco Corporation, please visit:

Masco Corporation, www.masco.com

MHPI (Masco Home Products India) | LinkedIn

Role Summary

The Data Engineer designs, builds, and operates the ingestion, transformation, and Lakehouse solutions that power Masco's enterprise POS and adjacent commercial data. This role delivers high-quality, reliable, and secure data pipelines against the standards and specifications set by the Enterprise Data Architect and the direction of the Data Engineering Leader. The engineer contributes across the full pipeline lifecycle — ingestion, data quality, transformation, and enrichment — including the pipeline-side execution of the enterprise attribution crosswalk. Depending on assignment, the Data Engineer may focus more heavily on ingestion or on quality and pipeline development, but the role covers all aspects.

What You'll Own

• Design, build, and maintain automated ingestion pipelines and multi-source integration for retailer, HQ, and BU data on the Databricks and Azure data stack.
• Develop RESTful APIs and API-based integrations for retailer and third-party data acquisition.
• Deliver critical-path pipelines and validation for priority data feeds.
• Integrate with orchestration tools and cloud or hybridstorage systems to enable end-to-end data workflows.
• Contribute to Lakehouse solutions using ACID-compliant storage layers, schema enforcement, and versioning for reliable data management.

Data Quality, Validation & Monitoring

• Run data-quality checks at the pipeline level — missing values, outliers, consistency, and cross-source reconciliation.
• Implement monitoring, validation, and automated notifications within the data lifecycle to protect performance, quality, and availability.
• Support pipeline monitoring, incident response, and continuous improvement in partnership with the Data Engineering Leader.
• Maintain change control and testing processes for modifications to pipelines and data models.

Attribution & Master Data Support

• Build the ingestion side of the attribution crosswalk and master data foundations against the Enterprise Data Architect's documented spec.
• Support maintenance of hierarchy mappings and attribute relationships through pipeline work.
• Surface data-quality issues, mapping exceptions, and structural gaps back to the Data Engineering Leader and Architect.

DevOps, Automation & Data Preparation

• Implement CI/CD pipelines for data engineering workflows and automate testing, deployment, and monitoring of data solutions.
• Develop, maintain, and secure tables, relationships, metrics, and calculations for analytical models supporting reporting and advanced analytics.
• Contribute to research and recommendations on data management products, services, and standards.

Documentation &Knowledge Management

• Document ingestion patterns, pipelines, validation rules, and metric definitions as part of the definition of done.
• Contribute pipeline lineage and technical metadata to the enterprise catalog.
• Follow the documentation standards established by the Data Engineering Leader and Enterprise Data Architect.

Education & Experience

• Bachelor's or master's degree in computer science, Engineering, Data Analytics, or a related field; equivalent professional experience considered.
• Substantial hands-on experience designing and building data engineering solutions on a major cloud platform, with emphasis on the Azure ecosystem.
• Experience delivering production pipelines and supporting them in an operational setting.

Technical Skills

• Advanced hands-on expertise with Databricks and the Azure data stack(Data Factory, Data Lake, Synapse, Analysis Services).
• Strong working knowledge of the medallion architecture (bronze / silver/ gold) for structuring Lakehouse solutions.
• Familiarity with Microsoft Fabric and how it fits alongside Databricks in a modernenterprise data platform.
• Advanced SQL / T-SQL (queries, stored procedures, functions, indexes, partitions, DDL, DML) and strong ETL/ELT design and build experience.
• Strong proficiency in Python (or Scala)for data engineering.
• Experience with RESTfulAPI development for data acquisition and ingestion from retailer and third-party sources.
• Working knowledge of distributed processing (Apache Spark) and Lakehouse principles — ACID storage, schema enforcement, versioning.
• Solid understanding of CI/CD,DevOps, and automation for data workflows.
• Strong understanding of Kimball dimensional modeling, including snapshot/transactional fact tables and conforming dimensions.
• Working knowledge of data constructs including keys, constraints, data types, normalization, referential integrity, and data quality analysis.
• Understanding of data governance, security, and compliance as they apply to data engineering.
• Detail-oriented and thorough. Considers all aspects of a data problem, including how the small pieces relate to the larger picture.
• Highly self-motivated and self-directed. Operates with minimaloversight on complex,hands-on data work.
• Delivery-focused. Owns commitments and follows through.
• Willingness to exploreand understand new and moderndata tools to add value to the enterprise POS and POS-related engineering space.
• Collaborative with the Data Engineering Leader, Enterprise Data Architect, and BI Developers on shared pipelines, models, and metric definitions.
• Continuous learner who stays current on modern data engineering practices and tools.
• Strong communicator who can explain technical detail clearly to both engineering and analytics peers.

Preferred Qualifications

• Experience in retail, consumer goods, or manufacturing analytics environments where POS, sell-in, inventory, and third-party retail data are core.
• Experience with Databricks Unity Catalog, data lineage, and observability tools.
• Familiarity with ML/AI integration (MLflow, Azure ML) and DataOps/MLOps practices.
• Experience with Microsoft Tabular models and the DAX language.
• Experience with at least one data analysis tool such as Power BI, Tableau, etc.

Other Mandatory Requirement:

• Must Have Broadband Availability: Minimum 30 MBPS -Only connections from national service providers such as Jio, Tata, Airtel, or Hathway Fiber are acceptable. Local or unverified internet providers are not aligned with MHPI IT policy and should not be used for official work.
• Home Office Setup (Mandatory): working space to accommodate two monitors +1 laptop (necessary equipment will be provided.)
• The candidate must have access to a quiet and dedicated home office environment that supports productive remote work.
• IT Asset Arrangement: All necessary equipment (based on the role)- including monitors, laptop, and supporting accessories will be provided by the company. Candidates must ensure safe and secure setup and usage of these assets.

This setup is non-negotiable and essential to ensure uninterrupted workflow, secure data handling, and effective collaboration. Candidates who do not meet this requirement will not be considered.