Essential Duties and ResponsibilitiesBuild, maintain, and optimize data pipelines utilizing Azure Data Factory, ensuring data is ingested, transformed, and delivered to Snowflake reliably for analyticsImplement monitoring, alerts, and testing of data pipeline performance, data quality metrics, and lineage to ensure trustworthy data deliveryTroubleshoot data issues and perform root cause analysis to proactively resolve operational issuesDocument data structures, processes, architectural decisions, and best practices for knowledge sharingDevelop, maintain, and optimize Snowflake objects (schemas, tables, views) and SQL transformations to produce curated, analytics‑ready datasetsCollaborate with analysts, stakeholders, and product owners to translate business needs into data requirements and stable technical implementationsEnable data for AI/ML use cases by preparing feature‑rich datasets, supporting feature engineering, and ensuring data consistency for model training and inferenceSupport 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 processesImplement version control practices for all data engineering code and documentationExperience and QualificationsBachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related field; or equivalent experience5+ 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 automationAI/ML Workflow ExperienceData preparation and feature engineering for machine learning modelsIntegration 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 optimizationProficiency with Git, Azure DevOps, and collaborative development best practicesExperience designing, developing, and deploying end‑to‑end pipelines using Azure Data FactoryWorking Conditions / Physical DemandsSitting at workstation for prolonged periods of time. The Data Engineer is responsible for supporting the development, maintenance, and optimization of data pipelines and analytics‑ready datasets.