About the job
Established in 2006, ProArch began as a close-knit team of IT professionals with a business mindset and a fervor for providing outstanding cloud technology solutions. Over the years, we have evolved into a global powerhouse, with a presence in the US, UK, and India, featuring a diverse range of experts in cloud computing, infrastructure, data analytics, cybersecurity, compliance, and software development. ProArch is the go-to partner for organizations seeking innovative guidance, seamless execution, and reliable ongoing support, helping them achieve their strategic vision.
Our services are structured into specialized practice areas that are meticulously designed to work together, enhancing each other's effectiveness. This interconnected methodology eliminates obstacles and fosters rapid growth and financial stability. Many clients utilize our comprehensive service offerings, creating a value chain that positively influences their entire organization, from IT to customer experience.
At ProArch, we are driven by the belief that technology has the power to transform. We aim to inspire innovation that impacts our people, clients, partners, and the communities we serve.
Job Description:
We are looking for a Senior Data Engineer with 3–5 years of experience to join our Data & Analytics practice. In this role, you will be responsible for designing and implementing scalable data solutions, optimizing performance, and enabling advanced analytics across cloud platforms. You will work with high-volume datasets and modern cloud-native technologies, collaborating with business stakeholders to develop robust and secure data ecosystems. This position also includes mentoring junior engineers and promoting 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 emphasizing scalability and performance.
- Manage Azure Data Lake for large-scale structured and unstructured data ingestion.
- Implement CI/CD pipelines and apply DataOps practices to enhance automation and reliability.
- Optimize data workflows to ensure high performance and reliability.

