The Solutions Delivery Engineer will design and build scalable data pipelines while managing the full data lifecycle for client deliveries. They are responsible for authoring data models, ensuring data quality through testing, and maintaining documentation for pipeline reliability.
Overview
Cherre is now a RealPage company, expanding our ability to deliver innovative data and technology solutions that transform the real estate industry. Join us as we build what’s next together.
Cherre is the leader in real estate data and insight. We connect decision makers to accurate property and market information, and help them make faster, smarter decisions. By providing a unique “single source of truth,” Cherre empowers customers to evaluate opportunities and trends faster and more accurately, while saving millions of dollars in manual data collection and analytics costs.
We’re looking for a passionate and driven Solutions Delivery Engineer to join our Solutions Delivery Team (SDT). In this role you’ll own the full data lifecycle for client delivery as part of a small, high-ownership team working at the cutting edge of real estate data services.
Responsibilities
Understand and translate client data requirements into clear, well-scoped technical tasks
Design and build scalable, maintainable data pipelines aligned to Cherre’s standards, spanning ingestion, transformation, and delivery
Author and maintain data models using dbt across BigQuery and Snowfl ake environments, managing schema evolution and version control
Contribute to a shared automation and tooling layer that enables the team to resolve issues and deliver connectors faster and more consistently
Conduct thorough testing and data validation to ensure accuracy, performance, and compliance with client standards
Participate in peer reviews to ensure quality, consistency, and adherence to best practices
Write clear documentation for each pipeline or transformation step, including data lineage, mapping logic, and assumptions
Monitor, support, and maintain data pipelines for ongoing reliability; iterate on solutions based on client feedback and evolving needs
Qualifications
Strong experience in database technologies and data warehousing
Experience in Python for backend development and data processing
A strong understanding of data modeling techniques (3NF, Dimensional, etc.) and their intended use cases
Hands-on experience with cloud data warehouse platforms like BigQuery, Snowfl ake, or Redshift
Experience with modern data transformation tools like dbt or Dataform
Experience with ELT/ETL frameworks and data ingestion pipelines at scale
Working knowledge of data quality frameworks, error handling, logging, and observability practices
Clear and professional communication to manage expectations, explain pipeline behavior, and resolve data discrepancies
Experience creating data lineage diagrams and documenting pipelines
Ability to design and lead data architecture decisions, including schema evolution, pipeline orchestration, and infrastructure-as-code (e.g., Terraform, Docker, Kubernetes)
Strong interpersonal skills to align stakeholders, manage ambiguity, and navigate evolving client expectations
Bonus Skills
Experience working with real estate data domains such as property, leasing, tenant, transactions, and capital markets
Demonstrated experience ingesting and transforming 3rd-party real estate market data (e.g., CoreLogic, CompStak, Yardi Matrix) and application data (e.g., Yardi, MRI, VTS, Realpage Onsite)
Familiarity with knowledge graph or graph database concepts
Experience with GraphQL APIs and metadata management
Knowledge of DevOps practices and cloud platforms like AWS or Google Cloud Platform (GCP)
Experience working in Agile environments with a focus on rapid iteration and continuous improvement
Deep experience with real estate data ecosystems, including canonical modeling across property management, leasing, and investment systems
Demonstrated success integrating bespoke and third-party real estate data at scale, ensuring consistency, integrity, and compliance with client data standards
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