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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr. Azure Data Engineer - **Company:** AI Engineers, Inc. - **Location:** Albany, NY, United States - **Experience:** Expert - **Contract:** Contract - **Skills:** Training Data, Agile Methodology, Artificial Intelligence, Data Analysis, Application Frameworks, Automation of Tests, Microsoft Azure, Software Quality, Continuous Integration, Data Architecture, Information Engineering, Extract Transform Load (ETL), Data Systems, Data Vault Modeling, Data Warehousing, DevOps, Dimensional Modeling, Information Technology Operations, Python (Programming Language), Machine Learning, Meta-Data Management, Power BI, Microsoft Copilot, Standard Sql, Azure Data Lake, SQL Databases, Enterprise Data Management, Azure Data Factory, Delivery Pipeline, Apache Spark, Generative AI, Git, Data Layers, Microsoft Fabric, Data Lakes, Pyspark, Information Technology, Deployment Automation, Integration Frameworks, Data Management, Tools for Reporting, Azure Synapse Analytics, Software Version Control, Data Pipelines, Databricks - **Published:** November 5, 2026 - **Apply:** https://www.dice.com/job-detail/962aff06-1d7c-4faf-b6b4-068295227c51 ## About the Role Candidates must be legally independent and authorised to work in the United States., Bachelor's degree in computer science, Engineering, Math or related field is required Master's degree is preferred 12+ years of experience in data engineering, data warehousing, ETL/ELT, or enterprise analytics platforms, with demonstrated experience delivering production-grade data solutions. Strong hands-on SQL skills and experience with Python and/or PySpark for data engineering workloads. Experience with Microsoft Fabric, Azure Data Factory, Azure Data Lake Storage, Synapse, Databricks, or comparable modern cloud data platforms. Strong understanding of data modeling, dimensional modeling, data lake/lakehouse patterns, and enterprise data architecture concepts. Experience implementing data quality, monitoring, observability, automated testing, and production support practices. Experience with Git, CI/CD, DevOps, and automated deployment practices. Strong analytical, troubleshooting, communication, and cross-functional collaboration skills. Experience with or knowledge of Agile Software Development methodologies Strong aptitude for learning new technologies and analytics techniques Communication skills and ability to develop and present solutions to all levels of management Must be able to interact effectively and patiently with customers especially while under pressure The ability to work on multiple projects/tasks simultaneously to meet project deadlines for self and others as required Ability to establish and maintain positive working relationships with other employees. Strong analytical and problem-solving skills, with strict attention to accuracy and detail, and the ability to evolve data into knowledge Passion for delivering highly available, robust BI solutions Knowledge of best practices and IT operations in an always-up, always-available service Preferred Skills Hands-on experience with Microsoft Fabric, OneLake, Fabric Data Engineering, Fabric Data Factory, and related platform capabilities. Experience with dbt Core and Data Vault 2.0. Experience with Microsoft Purview, metadata management, lineage, governance, and data catalog capabilities. Exposure to AI/ML, generative AI, Microsoft Copilot capabilities, or AI-assisted data and analytics engineering. ## Description The Senior Data Engineer is a hands-on technical role responsible for designing, developing, testing, deploying, and supporting scalable enterprise data solutions. The role will build and optimize data pipelines, data models, and analytics-ready data products using Microsoft Fabric and Azure technologies while ensuring strong engineering, security, reliability, data quality, observability, and production-readiness standards., Design, develop, test, deploy, troubleshoot, and optimize enterprise data pipelines and data solutions using Microsoft Fabric, Azure, SQL, Python, Spark, and related technologies. Build scalable ingestion, transformation, and data processing frameworks for batch and near-real-time workloads across multiple enterprise data sources. Develop and maintain Bronze, Silver, and Gold data layers and implement enterprise modeling patterns, including Data Vault 2.0 and dimensional/curated models where appropriate. Develop and maintain dbt models, reusable transformation frameworks, documentation, automated tests, and engineering standards. Partner with technical leadership to implement enterprise data architecture patterns, engineering standards, reusable frameworks, and platform guardrails. Implement CI/CD, source control, automated testing, deployment pipelines, and DevOps practices for data engineering solutions. Build data quality controls, reconciliation processes, monitoring, alerting, lineage, and observability to proactively identify and resolve data issues. Support production operations by troubleshooting pipeline failures, performance issues, data defects, and critical incidents; perform root-cause analysis and implement permanent fixes. Optimize data pipelines, SQL queries, Spark workloads, storage, and compute utilization for performance, scalability, reliability, and cost efficiency. Implement security, access controls, data protection, and governance requirements in partnership with architecture, governance, and security teams. Work closely with BI, analytics, and business teams to deliver trusted, analytics-ready data products and support Power BI semantic models and reporting solutions. Support migration and modernization of legacy ETL and enterprise data warehouse workloads to Microsoft Fabric while ensuring reconciliation, business continuity, and production readiness. Maintain technical documentation including solution designs, data mappings, operational runbooks, support procedures, and production-readiness artifacts. Apply AI-assisted engineering capabilities to improve developer productivity, accelerate SQL/Python development, support code quality, automate documentation and testing, and improve troubleshooting and root-cause analysis. Enable AI-ready data products by ensuring data is trusted, well-modeled, governed, documented, and accessible for AI, machine learning, natural-language querying, and advanced analytics use cases.