What we are looking for We are looking for an experienced Cloud Data Engineer with 5–7 years of expertise in designing and delivering scalable, high-performance data platforms on Microsoft Azure. Strong hands-on experience in building data pipelines, data modeling, and data warehousing is essential. The role requires understanding of distributed data processing using Azure Databricks/Spark, along with exposure to batch and real-time architectures. Proven experience in designing Azure data solutions, implementing data quality frameworks, and ensuring governance using Azure-native services is expected. Hands-on experience with DevOps/DataOps practices, CI/CD pipelines, and deployments on Azure is required. Strong problem-solving skills, ownership mindset, and ability to collaborate with stakeholders are key. What You'll Do Design and architect scalable data platforms and pipelines using Azure services such as Azure Data Factory, Azure Databricks, and ADLS. Build and optimize complex ETL/ELT pipelines for batch and real-time data processing using Databricks/Spark. Develop data models, data warehouse solutions, and optimized storage structures using Azure services. Implement data quality checks, validation frameworks, and monitoring to ensure reliable and accurate data. Optimize data pipelines and infrastructure for performance, scalability, and cost efficiency. Collaborate with business, analytics, and product teams to deliver data solutions aligned with business needs. What You'll Need 5–7 years of hands-on experience in data engineering with strong expertise across the Microsoft Azure ecosystem. Proven experience working with Azure Data Factory, Azure Databricks, ADLS, and Apache Spark for building scalable and efficient data pipelines. Strong programming skills in Python and advanced SQL, with the ability to write optimized, reusable, and production-grade code. Solid understanding of data warehousing concepts, data modeling techniques, and modern big data architectures. Hands-on experience with DevOps/DataOps practices, including CI/CD pipelines, Git-based version control, and deployment of data solutions in production environments. Good understanding of data quality frameworks, validation techniques, monitoring, and troubleshooting to ensure reliable and accurate data systems.

About ProcDNA

ProcDNA is a fast-growing, self-funded Analytics and Technology consulting firm established in January 2020, dedicated to delivering exceptional service to the life sciences industry. With deep expertise across Analytics, Insights, Data, and Technology, our team brings experience from leading life sciences companies, pharmaceutical organizations, and strong academic backgrounds to solve complex business challenges. We continuously attract top talent, expand our capabilities, and tailor innovative solutions to meet our clients' evolving needs. As a privately funded organization, we prioritize long-term partnerships, agility, and sustainable growth, enabling us to provide high-impact, client-focused solutions that help our partners succeed.