Senior Data Platform Engineer

UDR - Opening Doors to your future

About The Position

UDR, Inc. is seeking a Senior Data Platform Engineer to design, build, and maintain the company's enterprise data platform in Snowflake. This role involves implementing scalable and reliable data solutions for operational, analytical, and AI use cases. The engineer will develop data pipelines using the medallion architecture, create reusable data products, and enforce best practices for data transformation, quality, and governance. Collaboration with cross-functional teams, including business stakeholders and IT leadership, is essential to deliver data solutions that drive business value and promote self-service analytics.

Requirements

Bachelor’s degree in computer science, data science, data engineering, software engineering, information systems, or related field; or equivalent combination of education and experience required.
Minimum of five to seven years of experience in data engineering, analytics engineering, or similar role with demonstrated experience building enterprise data platforms.
Snowflake experience required.
Proven experience implementing data warehouses or data lakes in cloud environments.
Track record of building data pipelines that serve multiple consumption patterns including analytics, operations, and AI/ML applications.
Experience with dimensional modeling, ETL/ELT development, and data transformation at scale.

Nice To Haves

SnowPro Core Certification preferred.
Experience with Snowflake Cortex for semantic layer development preferred.
Proficiency integrating Snowflake with Power BI including DirectQuery and Import mode optimization preferred.
Experience building feature stores or preparing datasets for machine learning applications preferred.
Knowledge of CI/CD practices for data pipelines and automated testing frameworks preferred.
Experience with cloud platforms (AWS, Azure, or GCP) for data storage and processing preferred.

Responsibilities

Design and implement UDR’s enterprise data platform in Snowflake, replacing on-premises SQL systems and decentralized data silos with a unified, governed data environment using medallion architecture (Bronze/Silver/Gold layers).
Build and maintain scalable data pipelines from source systems through transformation to analytical and operational targets, ensuring data quality, consistency, and reusability.
Develop well-defined, tested, documented, and code-reviewed datasets optimized for diverse business use cases including operational queries, business intelligence, self-service analytics, and AI/ML applications.
Create dimensional models, data architectures, and semantic layers using Snowflake Cortex to support analytics initiatives and enable consistent business logic and metrics across applications.
Design and maintain integration patterns between Snowflake and Power BI, optimizing for DirectQuery and Import modes to balance performance and user experience.
Implement data orchestration workflows using Apache Airflow to automate ingestion, transformation, scheduling, and quality validation processes.
Establish data quality testing frameworks and governance controls including validation rules, error-handling, monitoring, and data lineage documentation to maintain data integrity and support self-service analytics.
Prepare feature stores and training datasets for AI and machine learning applications, ensuring data is structured and documented for model development.
Troubleshoot and resolve production issues related to data platform performance, pipeline reliability, and data quality.
Integrate data from vendor systems and external sources using Snowflake ingestion methods including APIs, file-based loads, and data sharing.
Partner with the Snowflake Administrator on platform optimization, security, resource management, and cost efficiency, and collaborate with business stakeholders to translate analytics requirements into scalable data solutions.
Contribute to engineering design sessions, code reviews, and the development of best practices for platform configuration, observability, testing, and operational readiness.
Evaluate and adopt new technologies and tools that improve data platform capabilities and operational efficiency.
Perform other duties as assigned or as necessary.