Data Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Design, construct, and sustain scalable data pipelines for feature extraction, training data generation, and model monitoring.

• Create and enhance data systems that facilitate analytics and machine learning workloads, incorporating data lakehouse and feature store technologies.

• Oversee production data pipelines, detect data quality issues or pipeline failures, and implement enhancements to ensure reliability and freshness.

• Engage in engineering design discussions and contribute to technical decisions regarding data architecture and pipeline implementation.

• Develop reusable data engineering components, including automated data quality checks, schema validation, and testing frameworks.

• Convert business requirements into scalable data solutions that empower analytics and machine learning applications.

• Optimize SQL queries, Spark workloads, and data processing pipelines to enhance performance and scalability.

• Collaborate with ML Engineers and cross-functional partners to uphold MLOps best practices, including data versioning, lineage, and reproducibility.

• Decompose technical tasks into manageable components and provide high-quality solutions within an agile team.

⛳️ Requirements

• 3+ years of professional experience in Data Engineering, Software Engineering, or a related discipline.

• 2+ years of practical experience in constructing and maintaining production data pipelines that support analytics, reporting, or machine learning workloads.

• Strong expertise in Python and SQL with a track record of developing production-quality data pipelines.

• Familiarity with modern data processing technologies such as Spark, Airflow, Kafka, or similar distributed data platforms.

• Experience with cloud-based data platforms such as Databricks, Snowflake, Delta Lake, or comparable lakehouse technologies.

• Knowledge of data modeling, data warehousing, and best practices in data governance.

• Understanding of machine learning data workflows, including training datasets, feature engineering, and data quality considerations.

• Experience in deploying and maintaining production data pipelines with monitoring, testing, and CI/CD practices.

• Strong analytical skills, attention to detail, and the ability to collaborate effectively across engineering and product teams.

• Excellent communication skills and a commitment to continuously learning new technologies and engineering methodologies.

🏝️ Benefits

• Health insurance

• 401(k) matching

• Flexible work hours

• Paid time off

• Employee discounts (Dell, Gympass, Telus EAP)