DATA ENGINEER do? Build and optimize scalable ETL/ ELT data pipelines using Azure Data Factory, Azure Data Lake, Synapse, and/or Snowflake. Develop and maintain SQL Server databases, stored procedures, views, and performance-tuned queries at large scale. Implement data quality checks, validation rules, reconciliation, and error-handling frameworks to ensure trusted data delivery. Troubleshoot production issues, perform root-cause analysis, and proactively monitor pipeline performance and reliability. Build ingestion frameworks for structured/unstructured data, including real-time streaming via Kafka/ Event Hub (as needed). Partner with BI teams to deliver governed datasets and models that support Power BI reporting and self-service analytics. Follow CI/ CD, change management, version control (Git), and release practices using Azure DevOps. Contribute to solution design, mentor junior engineers, and help drive best practices across data engineering standards. What is required? Education: Bachelor's degree in Computer Science or a related field required. Certifications: Azure/ AWS certifications or data engineering certifications preferred. Experience: Minimum of 8 years of experience in data engineering, ETL development, or related roles. Advanced SQL skills and strong experience with Microsoft SQL Server at very large scale (up to petabyte-class environments). Hands-on experience with Azure Data Factory, Azure Data Lake, Synapse Analytics, and Databricks. Strong programming skills in Python or Scala for transformation and automation. Experience supporting production workloads, troubleshooting issues, and optimizing systems. Working knowledge of Power BI datasets and reporting fundamentals. Practical experience with CI/ CD, version control, and DevOps workflows (Git, Azure DevOps, GitHub Actions), including pipelines from ingestion to reporting. Familiarity with data governance, security, and compliance practices. Skills and Abilities: Real-time streaming experience with Kafka or Azure Event Hub. Infrastructure as Code experience (e.g., Terraform). Containerization experience (Docker/ Kubernetes). Exposure to data science workflows or ML model integration. Experience with BI tools beyond Power BI (e.g., Tableau, Looker, Qlik). Strong analytical mindset and ability to troubleshoot complex data issues. Collaborative, adaptable, and comfortable working across cross-functional teams. Ownership mindset - delivers high-quality solutions with accountability Continuous learner - keeps up with cloud/data engineering best practices Strong communication skills - can explain technical concepts to non-technical audiences.