ASPCA is the American Society for the Prevention of Cruelty to Animals, and they are seeking a Senior Enterprise Data Warehouse Engineer to design, build, and maintain their enterprise data warehouse ecosystem. This role involves developing and maintaining data workflows, integrating master data management workflows, and ensuring data assets are accurate and reliable.

Responsibilities:

Architect, implement, and maintain dbt Core models across the medallion architecture, applying appropriate transformation patterns to meet operational, analytical, and dimensional data requirements
Integrate MDM workflows and reference data into medallion-layer transformations in alignment with enterprise data governance standards
Apply mastered entities, harmonized identifiers, survivorship rules, and standardized reference values to Gold-layer models as defined by enterprise MDM policies
Build data models that power enterprise analytics, reporting, and other downstream uses
Implement modeling best practices (e.g., naming conventions, documentation, testing, and lineage tracking) across all layers to ensure dbt Core models comply with enterprise governance standards and quality, performance, and security requirements
Optimize SQL code and dbt Core transformation logic to ensure efficient, scalable, and maintainable data pipelines
Design, build, and maintain robust pipelines that support data ingestion, transformation, and delivery while adhering to engineering standards for well-structured code, clear documentation, and idempotent processing
Implement and maintain scalable job orchestration, monitoring, alerting, automated testing, and error-handling capabilities
Preserve version control for pipeline code artifacts and support CI/CD workflows to ensure reliable deployment of changes
Troubleshoot pipeline problems, such as issues with data quality and concerns over data freshness, across Microsoft Fabric, dbt Core, and Snowflake
Conduct root cause analysis and proactively drive pipeline improvements
Work closely with the Data Management & BI team to align on definitions, requirements, and expectations, ensuring that engineered datasets are accurate, trusted, and analytics-ready
Collaborate with the Strategy & Research team to deliver granular, well-structured Silver-layer datasets that support statistical analysis, data science modeling, and operational insights
Support enterprise data governance and data quality through strong metadata practices and clear documentation of transformation logic
Identify opportunities to improve data workflows, automate processes, and reduce technical debt
Participate in code reviews, knowledge-sharing sessions, and team-wide initiatives that strengthen engineering quality and consistency
Contribute to the advancement of the ASPCA's data ecosystem by evaluating emerging tools and technologies

Requirements:

Excellent analytical and problem-solving skills, with a strong commitment to data quality
Ability to collaborate effectively with both technical and non-technical partners
Solid written and verbal communication skills, with the ability to clearly convey data and technical concepts
Comfortable operating in a highly distributed, cross-functional environment
Skilled at managing multiple priorities, shifting requirements, and changing timelines
Demonstrates curiosity, creativity, and a willingness to experiment and learn
Takes initiative and works independently while valuing teamwork
Welcomes feedback and proactively seeks opportunities to improve systems and workflows
Values diversity of thought and embraces an inclusive, collaborative team culture
Ability to exemplify ASPCA's core values and behavioral competencies
Expert-level proficiency in dbt Core is required, including advanced model design, macro development, custom tests, documentation practices, performance optimization, and integration with automated deployment pipelines
Advanced proficiency with Microsoft Fabric and/or Azure Data Factory for enterprise pipeline orchestration, monitoring, scheduled and event-driven workflows, Lakehouse integration, operational support, and troubleshooting of production data pipelines
Deep knowledge of data warehousing principles, including dimensional modeling, medallion architecture, and ELT transformation patterns
Strong SQL skills (Python is a plus)
Familiarity with Git/GitHub workflows and DevOps CI/CD practices
Experience with cloud-native or SaaS-based data engineering tools
High School Diploma or GED(Required)
5+ years of hands-on experience building, maintaining, and optimizing dbt Core transformation pipelines across medallion architectures in production environments
5+ years of experience designing, implementing, and supporting production data pipelines using Microsoft Fabric and/or Azure Data Factory, including orchestration, monitoring, operational support, and troubleshooting of enterprise data workflows
Demonstrated experience applying dimensional modeling and enterprise data modeling practices (e.g., star schemas, SCDs, conformed dimensions)
3–5 years of experience designing and maintaining data warehouse models and transformation workflows
3–5 years of experience working within modern cloud data warehouse environments; experience with Snowflake is strongly preferred
Experience working within structured DevOps workflows, version control, and automated delivery pipelines
Bachelor's degree in Computer Science, Information Systems, Data Analytics, Engineering, or a related field
Proficiency with Snowflake is strongly preferred, including warehouse configuration, performance tuning, query optimization, cost management, and implementing role-based access controls
Familiarity with Snowflake-native dbt Projects, including development, deployment, scheduling, monitoring, configuration management, and version upgrades of dbt workloads within Snowflake
Experience integrating mastered entities and reference data from commercial Master Data Management (MDM) platforms into enterprise data pipelines; Reltio preferred

Staff Forward Deployment Engineer

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