Sr. Data Engineer
Join a pioneering financial services company that is redefining homeownership through innovative, data-driven solutions. As a Senior Data Engineer, you will be instrumental in designing and building scalable data pipelines, transforming complex datasets, and ensuring the reliability of the data infrastructure. Working closely with data scientists and analysts, you will provide critical insights that shape strategic decisions. If you are passionate about data engineering and thrive in a fast-paced, collaborative environment, this role offers you the opportunity to contribute to cutting-edge projects and help shape the future of data architecture Requirements 5+ years of Python development experience. Desirable experience with AWS, including services like DynamoDB, S3, EMR, Glue, Athena, and Redshift. Knowledge of distributed computing engines, such as Apache Spark. Proficiency in both relational and non-relational databases, along with data modeling expertise. Experience setting up code pipelines using CloudFormation or Terraform. Familiarity with ETL pipeline development in OLAP and Data Warehouse environments. Hands-on experience with orchestration tools like Airflow. Understanding of serverless architectures and scalable application design principles. Proficiency with version control systems, particularly Git. Experience with Node.js , DevOps, and CI/CD practices is a plus. Background in cloud technologies (AWS/ Azure / GCP) and fintech or banking is desirable. Responsibilities Design and implement scalable data pipelines in a horizontally scaled environment. Collaborate with cross-functional teams to understand business needs and define transactional scenarios across multiple microservices. Write high-quality, maintainable code that meets established standards and best practices. Build strong relationships with clients by understanding their business model and delivering solutions that align with their goals. Influence and communicate effectively at all levels, including management and engineering teams. Utilize distributed computing systems to transform and manage large datasets efficiently. Work with cloud-based technologies such as AWS to support the company’s data infrastructure. Contribute to continuous integration/continuous delivery (CI/CD) pipelines and DevOps practices. Collaborate with data scientists to ensure the availability and accessibility of critical insights for decision-making. Originally posted on Himalayas