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# Sr. Data Engineer

- **Company:** Masco Corporation
- **Location:** Indianapolis, IN, United States
- **Experience:** Expert
- **Salary:** $88,700.0 - $139,260.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Data Analysis, Microsoft Azure, Cloud Computing, Cloud Storage, Data Architecture, Data Cleansing, Information Engineering, Data Governance, Data Infrastructure, Data Integration, Dataspaces, Dimensional Modeling, Distributed Computing Environment, Machine Learning, Modular Design, DataOps, Salesforce.Com, SAP (Applications), SQL Databases, Workflow Management Systems, Data Processing, Enterprise Software Applications, Feature Engineering, Azure Data Factory, Snowflake, Apache Spark, Data Layers, Pyspark, Data Analytics, Machine Learning Operations, Api Design, Data Pipelines, Databricks
- **Published:** July 31, 2026
- **Apply:** https://masco.wd1.myworkdayjobs.com/Masco/job/US---Indiana---Indianapolis/Sr-Data-Engineer_REQ53875

## About the Role

· 5+ years of experience in data engineering, data platform development, or related roles

· Proven experience designing and maintaining scalable data pipelines in a cloud-based environment

· Strong proficiency in SQL and Python for data transformation and pipeline development

· Hands-on experience with distributed data processing frameworks (e.g., PySpark, Spark)

· Experience working with APIs and integrating structured and unstructured data sources

· Strong understanding of data modeling concepts (e.g., dimensional modeling, fact/dimension design)

· Experience implementing data quality, validation, and monitoring solutions

· Ability to work across technical and business teams to deliver data-driven solutions

· Strong problem-solving skills with the ability to manage complex data environments

Preferred Qualifications

· Experience working with Databricks, lakehouse architectures, or similar platforms

· Exposure to AI/ML workflows, including feature engineering and model data preparation

· Familiarity with POS and retail data ecosystems (e.g., Home Depot, Lowe's, Walmart, Amazon)

· Experience with third-party syndicated data sources (e.g., Stackline, Datavations, SimilarWeb)

· Knowledge of orchestration tools (e.g., Airflow, Azure Data Factory)

· Experience with cloud platforms (Azure, AWS, or GCP)

· Background in analytics engineering or building semantic/curated data layers, E-Verify Participation Poster: English & Spanish

E-verify Right to Work Poster: English, Spanish

## Description

Delta Faucet Company is seeking a Senior Data Engineer to lead the design, development, and scaling of enterprise data pipelines that power omni-retail analytics, advanced modeling, and AI/ML initiatives. This role is responsible for managing a complex POS-driven data ecosystem spanning multiple retailers, APIs, third-party data providers, and internal enterprise systems. The ideal candidate will bring strong technical expertise and a strategic mindset to transform fragmented data workflows into a scalable, governed, and high-performing data platform.

This role plays a critical part in modernizing the organization's data architecture by enabling consistent, reliable, and feature-ready datasets that support statistical modeling, AI use cases, and executive decision-making. The Senior Data Engineer will work closely with analytics, data science, and business stakeholders to standardize data definitions, improve data quality, and reduce reliance on manual data preparation processes.

Key Responsibilities

· Design, build, and maintain scalable data pipelines integrating POS, eCommerce, third-party, and enterprise data sources

· Lead cross-platform data integration across APIs, file ingestion, cloud storage, and analytics environments

· Develop and manage end-to-end data workflows from ingestion through transformation to consumption

· Create and maintain feature-ready datasets to support statistical modeling, machine learning, and AI initiatives

· Standardize data models, schemas, and transformation logic across retailers and product hierarchies

· Implement automated data quality checks, validation frameworks, and monitoring processes

· Optimize data pipelines for performance, scalability, and cost efficiency

· Reduce manual data processing by automating ingestion and transformation workflows

· Collaborate with analytics and data science teams to translate business requirements into scalable data solutions

· Establish and enforce best practices for data governance, consistency, and documentation

· Support integration of enterprise systems such as SAP, Salesforce, and Snowflake into the analytics ecosystem

· Mentor and guide team members on data engineering best practices and scalable design patterns


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