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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Microsoft Power BI Technical Specialist - **Company:** HCL America Inc. - **Location:** Hartford, CT, United States - **Experience:** Expert - **Salary:** $172,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon S3, Business Analytics Applications, Data Analysis, Apache HTTP Server, Application Release Automation, Automation of Tests, Microsoft Azure, Big Data, Cloud Computing, Computer Programming, Information Engineering, Data Governance, Data Infrastructure, Data Integration, Extract Transform Load (ETL), Data Security, Data Systems, Data Visualization, Data Warehousing, DevOps, Github, Identity and Access Management, Python (Programming Language), Meta-Data Management, Performance Tuning, Query Optimization, Role-Based Access Control, Power BI, Cloud Services, DataOps, Azure Machine Learning, Azure Data Lake, Scala (Programming Language), SQL Databases, Data Streaming, Management of Software Versions, Web Application Frameworks, Azure Service Bus, Data Processing, Azure Data Factory, Flask (Web Framework), Apache Spark, Caching, Data Layers, Microsoft Fabric, Data Lakes, Pyspark, Data Lineage, Apache Flink, Real Time Data, Apache Kafka, Spark Streaming, Machine Learning Operations, Software Coding, Restful APIs, Terraform, Stream Processing, Azure Synapse Analytics, Data Pipelines, Serverless Computing, Jenkins, Databricks - **Published:** August 2, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=c4eb0dda56977ab6 ## About the Role 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. ## Description 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 ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Bringing AI Model Testing and Prompt Management to Your Codebase with GitHub Models](https://www.wearedevelopers.com/videos/1536-bringing-ai-model-testing-and-prompt-management-to-your-codebase-with-github-models) ## Related Articles - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Data Analyst Salary in the UK](https://www.wearedevelopers.com/magazine/278-data-analyst-salary-in-the-uk)