Why This Job is Featured on The SaaS Jobs
Data engineering roles in SaaS increasingly sit at the center of product decisioning, and this position is framed around building domain-heavy models for a specific product rather than operating generic reporting. The emphasis on transformation pipelines, testing, and performance suggests a data platform that supports ongoing product iteration, where trustworthy metrics and curated datasets become part of the application’s backbone.
For a professional building a SaaS career, the work maps closely to skills that transfer across modern subscription businesses: maintainable ELT patterns, analytics engineering practices, and the discipline of treating data assets as production systems. Collaboration with non-technical subject matter experts also mirrors how many SaaS companies translate complex customer domains into software, strengthening the ability to turn ambiguous requirements into durable schemas and reusable transformations.
This role is best suited to someone who enjoys owning data quality end to end and prefers engineering rigor over ad hoc analysis. It will fit a mid-level engineer who wants deeper exposure to dbt-style workflows, warehouse-centric architectures, and the communication required to align data definitions across teams, especially in regulated or terminology-heavy domains.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
Accorded is seeking an experienced data engineer to join our team. The ideal candidate will have a strong background in data engineering, proficiency in sql, python and modern data engineering tools, and an eagerness to learn our business domain.
Key Responsibilities
- Build and maintain data transformation pipelines with robust testing to ensure data integrity
- Design, implement, and maintain models with complex domain and business logic for Accorded’s Acumen product.
- Collaborate with cross-functional teams (such as our Actuarial Team) to understand data requirements and deliver high-quality solutions
- Optimize data storage and retrieval processes for improved performance and scalability
- Participate in code reviews and maintain high standards of code quality
Required Qualifications
- 2+ years of data engineering experience
- Strong proficiency in SQL and Python
- Extensive experience in managing data transformation pipelines
- Solid understanding of data modeling, data warehousing, and ETL processes
- Experience with version control systems (e.g., Git)
- Strong problem-solving skills and attention to detail
- Excellent communication skills and ability to work in a collaborative environment
Preferred Qualifications
- Experience with dbt (data build tool) is highly preferred
- Experience with tools like BigQuery and Snowflake
- Knowledge of healthcare data and related regulatory requirements (e.g., HIPAA)
Additional Information
- While healthcare experience is not strictly required, a willingness and enthusiasm to learn about the healthcare domain is essential
- You will work closely with subject matter experts to understand domain-specific requirements and challenges
- Accorded is located in the San Francisco Bay Area. Remote candidates living and authorized to work in the continental United States are eligible to apply.
We are looking for a candidate who is passionate about data engineering, committed to producing high-quality work, and eager to contribute to improving healthcare through data-driven solutions.
Accorded is an Equal Opportunity Employer. We are committed to creating a diverse and inclusive work environment and do not discriminate on the basis of race, color, religion, gender, gender identity, sexual orientation, national origin, genetics, disability, age, or veteran status.
