Senior Data Engineer, Data Platform
New
J
JobgetherHealthcare technology
Fully remote opportunity within the United States.Full-TimeSenior
Salary160,000 - 170,000 USD per year
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Job Details
- Experience
- 5+ years of professional experience building and operating production data systems.
- Required Skills
- PythonSQLData modeling
Requirements
- Bachelor’s degree or equivalent educational background.
- 5+ years of professional experience building and operating production data systems.
- Strong SQL and data modeling expertise, including grain, keys, historical data, and dimensional modeling.
- Experience designing and operating batch and asynchronous data pipelines.
- Solid understanding of distributed-system failure modes.
- Experience with cloud-based data platforms, including storage, query performance, scalability, and cost considerations.
- Strong understanding of data correctness, validation, idempotency, late-arriving and duplicate records, schema evolution, backfills, and reprocessing.
- Working proficiency in Python for developing pipelines, services, tooling, and automated tests.
- Ability to make controlled, reversible changes to production systems while protecting downstream consumers.
- Strong analytical and problem-solving skills, including working through ambiguous or incomplete requirements.
- Clear written and verbal communication skills, including explaining technical decisions and tradeoffs to stakeholders outside engineering.
Responsibilities
- Own production data systems and key data domains across ingestion, validation, storage, transformation, and downstream delivery.
- Design ingestion patterns for incomplete, duplicated, delayed, malformed, and evolving data from internal and external sources.
- Define data contracts between source systems, ingestion processes, transformations, and downstream consumers.
- Design and maintain data models with well-defined grain, keys, history, and business definitions.
- Safely evolve schemas, pipelines, and datasets while supporting retries, backfills, replay, and reprocessing.
- Improve pipeline performance, observability, monitoring, and failure handling.
- Partner with Engineering, Product, Operations, and Analytics to determine how data should be represented.
- Make architecture and production decisions balancing reliability, scalability, maintainability, performance, and cost.
- Document technical decisions and communicate data architecture, changes, and tradeoffs to technical and non-technical stakeholders.
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