Data Engineer

Strong experience with Azure data services (ADF, Databricks, Synapse, Azure SQL, Azure Storage). Proficiency in Python for data engineering and ML development. Experience with ML frameworks (scikit-learn, TensorFlow, PyTorch). Hands-on experience building ETL/ELT pipelines using Spark, Delta Lake, or similar. Strong SQL skills, including schema design, data modeling, and performance tuning. Solid understanding of version control, CI/CD practices, and modern development workflows. Ability to work effectively in cross-functional teams. Strong analytical thinking, troubleshooting abilities, and attention to detail. Experience supporting enterprise-scale environments or high-volume data systems. 6+ years of experience in data engineering or similar cloud/ML-focused roles (preferred). Experience with distributed systems or large-scale data architectures (preferred). Familiarity with ML lifecycle tools (MLflow, Azure ML) (preferred). Exposure to data governance, cataloging, or lineage solutions (preferred). Understanding of DevOps practices, infrastructure automation, or cloud security (preferred).

Build and maintain large-scale data pipelines for analytics, reporting, and ML. Develop and optimize data workflows using Azure services (Data Factory, Databricks, Functions, Storage). Collaborate with data scientists for feature engineering, dataset preparation, and ML model deployment. Integrate, cleanse, and transform data from various sources. Design and implement scalable data models and storage patterns for batch and near-real-time processing. Support ML/AI efforts using Python, Spark, MLflow, scikit-learn, TensorFlow, or PyTorch. Monitor pipeline performance, troubleshoot failures, and ensure data quality. Contribute to CI/CD processes for data engineering and ML automation. Document data flows, integrations, design decisions, and best practices. Partner with cloud teams, software engineers, and stakeholders to deliver data solutions.