Analytics Engineer

Masco Corporation•Indianapolis, IN

4d•$88,700 - $139,260•Onsite

About The Position

At Delta Faucet, we are committed to transforming everyday experiences with water through innovative products and exceptional service. As a leader in the plumbing industry, we take pride in our dedication to quality, sustainability, and customer satisfaction. We believe that diverse perspectives strengthen our mission to create solutions that inspire and elevate the lives of all our customers. We welcome individuals from all backgrounds to join us on this journey toward inclusivity and excellence. Your Role at Delta Faucet Are you passionate about building high-quality data foundations that power analytics, business intelligence, and AI solutions? If you are interested in enabling data-driven decision making for a growth-focused consumer products brand, this is an opportunity to join the market-leading Delta Faucet Company team as we scale our analytics capabilities to unlock strategic insights. Reporting to the Manager of Data Science and Analytics, this role will work with analysts, data science, and domain specialists across the business to deliver assets that support high-impact use cases. You will design and develop scalable data models, transform complex data into usable structures, and partner with stakeholders to ensure data is accessible, reliable, and aligned with business needs to drive the company’s growth initiatives.

Requirements

4+ years of experience writing and optimizing complex SQL for large-scale data transformation and analytics-ready data modeling.
3+ years of experience designing and maintaining data models that support multiple downstream use cases (e.g., BI, analytics, data science).
3+ years of experience working with cross-functional stakeholders to translate data requirements into scalable data assets.
2+ years of experience using Python for data manipulation, transformation, or analytics enablement.
2+ years of experience working with BI tools (e.g., Power BI), including enabling curated datasets, semantic models, or reporting layers.
Strong ability to communicate technical concepts to non-technical stakeholders and partner effectively across teams.
Bachelor’s degree in Statistics, Mathematics, Computer Science, Data Science, Economics, or a related quantitative field.

Nice To Haves

2+ years of experience supporting data ingestion or integration within cloud-based data platforms.
1+ year of experience working in Databricks, Spark, or similar modern data platforms.
1+ year of experience working within or supporting Power BI governance frameworks, including semantic modeling, dataset reuse, and governed self-service enablement.
1+ year of experience supporting AI, GenAI, or advanced analytics use cases through data modeling or pipeline development.
Exposure to federated or embedded BI models and analytics operating frameworks.
Experience providing analytics enablement, training, or standards guidance to business users.
Familiarity with layered data architectures (e.g., bronze/silver/gold medallion architecture).

Responsibilities

Contribute to and help operationalize data ingestion and integration efforts for internal and external data sources, including coordination with vendors and cross-functional partners.
Design, build, and maintain analytics-ready data models that support reporting, market intelligence, advanced analytics, and AI applications.
Develop and maintain data transformations and logic to operationalize new data domains and improve data quality, consistency, and usability.
Partner with analytics, data science, and business teams to ensure data assets align with analytical requirements and decision-making needs.
Support and contribute to platform-based analytics and AI initiatives (e.g., Databricks), including support for GenAI and advanced analytics solutions.
Enable downstream analytics by providing well-structured datasets, documentation, and guidance for analysts, BI developers, and self-service users.
Ensure data quality, reliability, and consistency through testing, validation, and monitoring of data pipelines and analytical datasets.
Support and promote analytics best practices, including data modeling standards, semantic modeling, dataset standardization, and governed self-service BI (e.g., Power BI).
Collaborate with the broader analytics community to share learnings, improve standards, and continuously enhance analytics capabilities.