[Hiring] Data Engineer @Tebra

Role Description

As a Data Engineer focused on AI/ML, you’ll build, maintain, and optimize the data infrastructure that powers Tebra’s intelligent features. You’ll partner closely with Machine Learning Engineers, Data Scientists, and Software Engineers to transform complex healthcare data into high-quality datasets and real-time features that enable machine learning models.

This is a hands-on engineering role where you’ll contribute to scalable data pipelines, improve data quality, and help ensure our AI systems are powered by reliable, performant, and well-governed data. You’ll work on modern data platforms and gain experience building solutions that support both model training and production inference.

Your Area of Focus

• 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.

Qualifications

• 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.

Requirements

• Strong proficiency in Python and SQL.
• Experience with modern data processing technologies.
• Experience with cloud-based data platforms.
• Understanding of data modeling and governance best practices.

Benefits

• Competitive and fair compensation.
• Variable pay opportunities.
• Robust benefits package.
• Discounts for work-from-home basics through Dell.
• Access to health and fitness resources through Gympass.
• Mental health resources through Telus Employee Assistance Program.

Company Description

Tebra is the only all-in-one EHR+ platform built exclusively for independent healthcare practices. Designed to replace the clunky, fragmented tools built for corporate systems, Tebra connects EHR software, billing, automation, telehealth solution, and marketing — so providers can spend less time on admin and more time with patients.

More than 42,000 private practices trust Tebra to streamline operations, increase revenue, and reduce burnout — helping clinicians leave work on time and rediscover their purpose.