Summary:
Overview
The Senior Enterprise Data Engineer will play a critical role in designing, building, and maintaining the ASPCA’s enterprise data warehouse ecosystem. This role will be part of the Enterprise Data Engineering Operations team, which sits within the broader Product, Data, and Reporting Solutions (PDRS) department. The Engineer will develop and maintain dbt Core models and data workflows across the medallion architecture, integrate master data management (MDM) workflows into enterprise pipelines, and partner with cross-functional stakeholders to support the ASPCA’s transition from siloed systems to a centralized enterprise data solution.
Reporting to the Director, Enterprise Data Engineering Operations, the Senior Enterprise Data Engineer will ensure that data assets are accurate, performant, reliable, and aligned with organizational standards. The ideal candidate will combine strong technical skills with a collaborative mindset, thriving in a distributed data environment where clarity, consistency, and data quality are essential. The ideal candidate will bring deep hands-on expertise with dbt Core, strong dimensional and medallion data modeling skills, and substantial experience developing and supporting enterprise data pipelines using Microsoft Fabric and Azure Data Factory.
Who We Are
The Information Technology (IT) department supports and improves a broad portfolio of technologies to support our staff. IT ensures that all ASPCA staff, partners, and communities have the systems needed to work effectively and efficiently to improve animal welfare. The sub-teams within IT include Product, Data and Reporting Solutions, Operations and Information Security, Technical Support, Enterprise Architecture, and Business Operations.
What You’ll Do
Senior Enterprise Data Engineer reports directly to the Director, Enterprise Data Engineering Operations, and has no direct reports.
Where and When You’ll Work
This remote‑based position (which requires travel, as described below) is open to all eligible candidates based within the United States.
- Must be available for occasional off-hours support for critical data pipelines
- Ability and willingness to travel up to 5% of the time as needed
Interview Information
Our interview process will include:
- a reference check early in our process, during which we will ask you to provide 5 references, 2 of which must be at the manager level.
- an in-person interview component, likely in New York City.
What You’ll Get
Compensation
Starting pay for the successful applicant will depend on a variety of factors, including but not limited to education, training, experience, location, business needs, internal equity, market demands or budgeted amount for the role. The target hiring range is for new hire offers only, and compensation may increase beyond the maximum hiring range based on performance over time. The maximum of the hiring range is reserved for candidates with the highest qualifications and relevant experience. The expected hiring salary ranges for this role are set forth below and may be modified in the future.
- $138,000-143,000 annually
For more information on our benefits offerings, visit our website.
Benefits
At the ASPCA, you don’t have to choose between your passion and making a living. Our comprehensive benefits package helps ensure you can live a rewarding life at work and at home. Our benefits include, but are not limited to:
- Affordable health coverage, including medical, employer-paid dental and optional vision coverage.
- Flexible time off that includes vacation time, paid personal time, sick time, bereavement time, paid parental leave, and 10 company paid holidays that allows you even more flexibility to observe the days that mean the most to you.
- Competitive financial incentives and retirement savings, including a 401(k) plan with generous employer contributions — we match dollar-for-dollar up to 4% and provide an additional 4% contribution toward your future each year.
- Robust professional development opportunities, including classes, on-the-job training, coaching and mentorship with industry-leading peers, internal mobility, opportunities to support in the field and so much more.
Responsibilities:
Responsibilities
Responsibility buckets are listed in general order of importance. They include, but are not limited to:
Medallion Layer Data Modeling and Development
- 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
Pipeline Orchestration and Operations
- 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
Collaboration, Alignment, and Data Quality
- 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
Qualifications
- 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
Technical Requirements
- 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 required
- Python experience preferred
- Familiarity with Git/GitHub workflows and DevOps CI/CD practices
- Experience with cloud-native or SaaS-based data engineering tools
- 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, is strongly preferred
Language
Education and Work Experience
- High School Diploma, GED, or equivalent experience (Required); Bachelor's degree in Computer Science, Information Systems, Data Analytics, Engineering, or a related field preferred
- 5+ years of hands-on experience building, maintaining, and optimizing dbt Core transformation pipelines across medallion architectures in production environments (Required)
- 5+ years of experience designing, implementing, and supporting production data pipelines using Microsoft Fabric or Azure Data Factory, including orchestration, monitoring, operational support, and troubleshooting of enterprise data workflows (Required)
- Demonstrated experience applying dimensional modeling and enterprise data modeling practices (e.g., star schemas, SCDs, conformed dimensions)preferred
- 3+ years of experience designing and maintaining data warehouse models and transformation workflows (Required)
- 3+ years of experience working within modern cloud data warehouse environments required; experience with Snowflake is strongly preferred
- Experience integrating mastered entities and reference data from commercial Master Data Management (MDM) platforms into enterprise data pipelines preferred; Reltio preferred
- Experience working within structured DevOps workflows, version control, and automated delivery pipelines preferred
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