Data Engineer (Remote)

The Data Engineer is responsible for supporting the development, maintenance, and optimization of data pipelines and analytics-ready datasets. You will be collaborating across multiple teams and stakeholders to solve complex problems and support data-driven initiatives. Essential Duties and Responsibilities Build, maintain, and optimize data pipelines utilizing Azure Data Factory, ensuring data is ingested, transformed, and delivered to Snowflake reliably for analytics.

Implement monitoring, alerts, and testing of data pipeline performance, data quality metrics, and lineage to ensure trustworthy data delivery. Troubleshoot data issues and perform root cause analysis to proactively resolve operational issues. Document data structures, processes, architectural decisions, and best practices for knowledge sharing.

Develop, maintain, and optimize Snowflake objects (schemas, tables, views) and SQL transformations to produce curated, analytics-ready datasets. Collaborate with analysts, stakeholders, and product owners to translate business needs into data requirements and stable technical implementations. Enable data for AI/ML use cases by preparing feature-rich datasets, supporting feature engineering, and ensuring data consistency for model training and inference.

Support deployment and operationalization of machine learning models by integrating pipelines with ML workflows (e.g., batch/real-time scoring). Continually improve ongoing reporting and analytics, automating or simplifying self-service or manual processes. Implement version control practices for all data engineering code and documentation.

Experience and Qualifications Bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related field; or equivalent experience. 5+ years of experience in data engineering or business intelligence roles working with ETL, data modeling, data architecture, and developing pipelines and applications for analytics (e.g., BI, reporting, machine learning, deep learning). Solid programming skills in advanced SQL, Python, or other programming languages for data processing and automation. Experience Supporting AI/ML Workflows Data preparation and feature engineering for machine learning models.

Integration of data pipelines with ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch, or similar). Understanding of model lifecycle concepts (training, validation, deployment, monitoring). Expertise working with Snowflake for data warehousing, including experience with schema design, performance tuning, and optimization.

Proficiency with Git, Azure DevOps, and collaborative development best practices. Experience designing, developing, and deploying end-to-end pipelines using Azure Data Factory. Working Conditions / Physical Demands Sitting at workstation for prolonged periods of time.

Extensive computer work. Workstation may be exposed to overhead fluorescent lighting and air conditioning. Fast paced work environment.

Operates office equipment including personal computer, copiers, and fax machines. This job description is not intended to be and should not be construed as an all-inclusive list of all the responsibilities, skills or working conditions associated with the position. While it is intended to accurately reflect the position activities and requirements, the company reserves the right to modify, add or remove duties and assign other duties as necessary.

External and internal applicants, as well as position incumbents who become disabled as defined under the Americans with Disabilities Act, must be able to perform the essential job functions (as listed here) either unaided or with the assistance of a reasonable accommodation to be determined by management on a case by case basis. #J-18808-Ljbffr