Senior Data Engineer - Python Job

YASH Technologies is a leading technology integrator specializing in helping clients reimagine operating models, enhance competitiveness, optimize costs, foster exceptional stakeholder experiences, and drive business transformation.

At YASH, we’re a cluster of the brightest stars working with cutting-edge technologies. Our purpose is anchored in a single truth – bringing real positive changes in an increasingly virtual world and it drives us beyond generational gaps and disruptions of the future.

We are looking forward to hire Python Professionals in the following areas :

Job description:

Experience required: 5-12 years

We are seeking a highly motivated and technically skilled Senior Data Engineer with strong expertise in data pipeline development and advanced data modelling to join our data team. The ideal candidate will have extensive experience on designing, building, and refactoring scalable data pipelines and data models within a modern data engineering ecosystem using Palantir Foundry, AWS & Snowflake, along with broader expertise in data architecture, governance, and processing. This role offers the opportunity to work on cutting-edge data platforms and deliver impactful solutions for enterprise data integration, transformation, and advanced analytics.

Key Responsibilities:

• Design, implement, and maintain scalable and reliable data pipelines using Palantir Foundry, AWS
• Solid experience in web front-end development, web-services.
• Some exposure to business analytics, financial forecasting, or a related domain would be preferable.
• Collaborate with data scientists, analysts, and business stakeholders to understand data requirements and translate them into technical solutions.
• Build reusable data assets, integrate data from multiple sources, and ensure data quality, integrity, and security.
• Develop and maintain transformation workflows, ontology models, and data governance frameworks within Foundry.
• Optimize data pipelines for performance, scalability, and cost efficiency.
• Troubleshoot, monitor, and resolve data issues and pipeline failures in real-time.
• Implement best practices in data versioning, lineage, and observability.
• Assist in defining data architecture strategies and tooling roadmaps.
• Mentor junior engineers and contribute to team knowledge sharing and documentation.