Vida Health - Senior Data Engineer- Data Platform
Requirements
• Bachelor's degree at a minimum. • 5+ years building production data systems. • Strong SQL and data modeling fundamentals, including grain, keys, history, dimensional modeling, and the tradeoffs between different representations of the same data. • Experience designing and operating batch and asynchronous data pipelines, including the failure modes that appear in distributed systems. • Experience with a cloud data platform and an understanding of storage, query performance, and cost. • Strong instincts around data correctness, including validation, idempotency, late and duplicated records, schema changes, backfills, and reprocessing. • Working proficiency in Python for pipelines, services, tooling, and tests. • The ability to change production systems in ordered, reversible steps without breaking their consumers. • Clear written communication, including documenting technical decisions and explaining changes to people outside of engineering. • You do not need previous healthcare experience or experience with every technology we use. • $160,000 - $170,000 a year • Vida is proud to be an Equal Employment Opportunity and Affirmative Action employer. • Diversity is more than a commitment at Vida—it is the foundation of what we do. All qualified applicants will receive consideration for employment without regard to race, color, ancestry, religion, gender, gender identity or expression, sexual orientation, marital status, national origin, genetics, disability, age, or Veteran status. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. • We seek to recruit, develop and retain the most talented people from a diverse candidate pool. We don’t just accept differences — we celebrate them, we support them, and we thrive on them for the benefit of our employees, our platform and those we serve. Vida is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. • We do not accept unsolicited assistance from any headhunters or recruitment firms for any of our job openings. All resumes or profiles submitted by search firms to any employee at Vida in any form without a valid, signed search agreement in place for the specific position will be deemed the sole property of Vida. No fee will be paid in the event the candidate is hired by Vida as a result of the unsolicited referral. • *Vida is authorized to do business in many, but not all, states. If you are not located in or able to work from a state where Vida is registered, you will not be eligible for employment. Please speak with your recruiter to learn more about where Vida is registered. • Please note: Applicants must be authorized to work in the U.S. as Vida is unable to sponsor work visas for any position. • All Vida Employees must reside in/be able to work from the U.S.- international work is prohibited. Job postings at Vida will remain open through end of year, until filled. • We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Responsibilities
• Own production data systems and domains from ingestion through delivery. • Design ingestion patterns that handle incomplete, duplicated, delayed, malformed, and changing partner data. • Define the contracts between source systems, ingestion, transformation, and downstream consumers. • Design data models with clear grain, keys, history, and definitions so the same question doesn't get two different answers. • Evolve schemas, pipelines, and datasets safely, with retries, backfills, and replay that don't corrupt or duplicate data. • Improve pipeline performance, observability, and failure handling so you can tell when something is wrong and why. • Work with Engineering, Product, Operations, and Analytics to figure out what the data needs to represent when requirements are incomplete or messy.
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