Data Engineer

🕒 July 27

🇺🇸 United States – Remote

💵 $128k - $145.2k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

🚰 Data Engineer

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Tebra

WebsiteLinkedInAll Job Openings

501 - 1000 employees

🏥 Healthcare

💼 Consulting

📣 Marketing

💰 Debt Financing on 2022-07

Healthcare • Consulting • Marketing

Tebra is a comprehensive technology platform designed to streamline the operations of independent healthcare practices. It provides an all-in-one solution that optimizes practice efficiency by integrating electronic health records, revenue cycle management, and patient scheduling with over 130 healthcare systems. Tebra offers tools for practice growth, patient experience, care delivery, billing and payments, and data insights. The platform helps practices attract more patients, manage their reputation, enhance patient experience, and automate operations while ensuring full security and HIPAA compliance. Tebra is tailored for family medicine, pediatrics, primary care, and psychology specialties, enabling them to deliver better care, get paid faster, and gain deeper insights through data-driven practice management.

📋 Description

• Design, build, and maintain scalable data pipelines for feature extraction, training data generation, and model monitoring. • Develop and enhance data systems that support analytics and machine learning workloads, including data lakehouse and feature store technologies. • Monitor production data pipelines, identify data quality issues or pipeline failures, and implement improvements to ensure reliability and freshness. • Participate in engineering design discussions and contribute to technical decisions around data architecture and pipeline implementation. • Build reusable data engineering components, including automated data quality checks, schema validation, and testing frameworks. • Translate business requirements into scalable data solutions that enable analytics and machine learning use cases. • Optimize SQL queries, Spark workloads, and data processing pipelines to improve performance and scalability. • Collaborate with ML Engineers and cross-functional partners to support MLOps best practices, including data versioning, lineage, and reproducibility. • Break down technical work into manageable tasks and deliver high-quality solutions within an agile team.

🎯 Requirements

• 3+ years of professional experience in Data Engineering, Software Engineering, or a related field. • 2+ years of hands-on experience building and maintaining production data pipelines supporting analytics, reporting, or machine learning workloads. • Strong proficiency in Python and SQL with experience developing production-quality data pipelines. • Experience with modern data processing technologies such as Spark, Airflow, Kafka, or similar distributed data platforms. • Experience working with cloud-based data platforms such as Databricks, Snowflake, Delta Lake, or equivalent lakehouse technologies. • Understanding of data modeling, data warehousing, and data governance best practices. • Familiarity with machine learning data workflows, including training datasets, feature engineering, and data quality concepts. • Experience deploying and supporting production data pipelines with monitoring, testing, and CI/CD practices. • Strong problem-solving skills, attention to detail, and the ability to collaborate effectively across engineering and product teams. • Excellent communication skills and a desire to continuously learn new technologies and engineering practices.

🏖️ Benefits

• Health insurance • 401(k) matching • Flexible work hours • Paid time off • Employee discounts (Dell, Gympass, Telus EAP)