Microsoft Power BI Technical Specialist

US Citizen, Senior Data Engineer with 11+ years of experience designing, building, and optimizing enterprise-scale data platforms, Lakehouse architecture, and analytics solutions in Azure cloud ecosystems. Proven expertise in developing scalable data pipelines, ETL/ELT frameworks, and cloud-native data solutions using Python, Spark, Azure Databricks, Azure Data Factory, Iceberg, Redshift, Synapse, S3, and ADLS Gen2 to support high-volume data processing, advanced analytics, and business intelligence., Design and implement scalable Lakehouse and data platform architectures leveraging Apache Iceberg, Delta Lake, and cloud-native storage solutions to enable ACID transactions, schema evolution, data versioning, and multi-engine analytics. Develop and maintain high-performance ETL/ELT pipelines using Python, PySpark, Azure Databricks, Azure Data Factory (ADF), and Spark for batch and real-time data processing. Design and optimize data models, data warehouses, and semantic layers to support enterprise reporting, self-service analytics, and AI/ML workloads. Build cloud-native solutions using Azure (Databricks, Synapse Analytics, ADLS Gen2, Event Hubs, Azure Functions) to deliver scalable and cost-effective data solutions. Implement robust data governance, lineage, quality, and compliance frameworks through automated validation, data contracts, metadata management, and monitoring solutions. Optimize large-scale data processing workloads through partitioning, clustering, compaction, caching, and query tuning techniques to improve performance and reduce infrastructure costs. Establish and maintain DataOps and DevOps best practices, including CI/CD pipelines, Infrastructure-as-Code (Terraform), automated testing, monitoring, and release automation. Develop secure data platforms using IAM, RBAC, KMS/CMK encryption, Private Endpoints, Lake Formation, and Microsoft Purview, ensuring compliance with enterprise security and regulatory requirements. Build and support streaming and real-time data solutions using Kafka, Kinesis, Event Hubs, Flink, and Spark Streaming. Enable AI/ML and advanced analytics initiatives by integrating data platforms with SageMaker, MLflow, Azure Machine Learning, and MLOps frameworks. Collaborate closely with business stakeholders, architects, data scientists, analysts, and engineering teams to translate complex business requirements into scalable, reliable, and high-performing data solutions.

Provide technical leadership, mentoring engineering teams, and establish best practices for architecture, coding standards, performance optimization, and cloud-native data engineering.

Skill Requirements

Programming & Data Engineering: Python, SQL, PySpark, Scala, ETL/ELT, Data Modeling, Data Warehousing

Azure Technologies: Azure Databricks, Azure Synapse Analytics, Azure Data Factory (ADF), ADLS Gen2, Event Hubs, Azure Functions, Azure Machine Learning, Microsoft Purview

Lakehouse Technologies: Apache Iceberg, Delta Lake, Hudi, Data Lake Architecture

DataOps & Automation: Terraform, GitHub Actions, Azure DevOps, Jenkins, Airflow, dbt, Great Expectations

Analytics & BI: Power BI, Semantic Models, Data Visualization, Executive Reporting

Governance & Security: RBAC, IAM, KMS/CMK, Data Lineage, Data Governance, Compliance Frameworks

Experience with Power BI or other data visualization tools. Exposure to cloud certifications (e.g., Azure Cloud Practitioner). Knowledge of REST APIs and web frameworks (e.g., Flask). Experience with stakeholder engagement and requirements gathering. Additional experience in project management or leadership roles is a plus. Additional Attributes Strong organizational and multitasking abilities. Willingness to learn new technologies and adapt to changing requirements. Ability to analyze business requirements and design robust data pipelines, data models, and ETL/ELT processes to enable reliable data-driven decision-making.

Minimum Salary (US): 87000