Senior Data Engineer

Senior Data Engineer

Founded in 2006, ProArch began as a tight-knit team of business-minded IT professionals passionate about delivering exceptional cloud-based technology solutions. Today, we've grown into a global force spread across US, UK and India into multidisciplinary experts spanning cloud, infrastructure, data analytics, cybersecurity, compliance, and software development. Organizations turn to ProArch for forward-thinking guidance, seamless implementation, and dependable ongoing support partnering with us to reach every milestone on the path to their vision.

Our services consist of specific practice areas that are methodically designed to complement and enhance each other's effectiveness. This interconnected approach removes roadblocks and drives rapid growth and financial stability. Many of our clients leverage the full scope of our services, creating a supply chain of value that impacts the entire organization from IT to customer experience.

At our core, we believe in the power of technology to transform. We're passionate about inspiring innovation that shapes our people, our clients, our partners, and the communities we serve.

ProArch is seeking a Senior Data Engineer with 3–5 years of experience to join our Data & Analytics practice. In this role, you will design and deliver scalable data solutions, optimize performance, and enable advanced analytics across cloud platforms. You will work with large-scale datasets, modern cloud-native technologies, and collaborate with business stakeholders to build reliable and secure data ecosystems. This position also involves mentoring junior engineers and driving best practices in DataOps and performance optimization.

Responsibilities

• Design, develop, and optimize data pipelines using Azure Synapse Analytics, Spark Pools, and Azure Data Factory.
• Write efficient Python and SQL code with a focus on scalability, performance, and optimization.
• Manage Azure Data Lake for structured and unstructured data ingestion at scale (volume, variety, velocity).
• Implement CI/CD pipelines and follow DataOps practices for automation and reliability.
• Optimize data partitioning, versioning, and storage/compute costs for large-scale workloads.
• Ensure high-quality solutions through unit testing frameworks and automated validation.
• Collaborate with business teams to support analytics, reporting, and data science initiatives.
• Mentor junior engineers, sharing knowledge and promoting engineering excellence.