Data Engineer

As a Data Engineer at our company, you will have the opportunity to contribute significantly to mid- to large-scale projects by showcasing your technical expertise. Your role will involve architecting and implementing cloud-based data solutions, designing and optimizing PySpark-based data pipelines, and contributing to end-to-end application development. You will also play a key role in ensuring compliance with coding standards and best practices for AI-assisted code generation. Your responsibilities will include: - Acting as a technical leader by making significant contributions to project planning and implementation - Demonstrating versatility in knowledge and applying strong technical skills to architect and implement cloud-based data solutions - Validating requirements, performing business and technical analysis, and designing cloud-native applications - Writing optimized PySpark-based data pipelines and contributing to end-to-end application development - Ensuring compliance with coding standards and best practices for AI-assisted code generation - Utilizing GenAI tools like Cursor, Claude, and other LLMs to decompose complex requirements and auto-generate UI, API, and database scripts - Introducing and forecasting the impact of new software design patterns, AI-driven development workflows, and emerging cloud technologies - Mentoring team members and fostering collaborative learning environments Preferred strengths for this role include proficiency in GenAI-powered development, strong experience with SQL, relational databases, and GIT, as well as expertise in Microsoft Azure cloud services and familiarity with serverless architectures and big data frameworks like Apache Spark. To be successful in this role, you should have: - 7+ years of software engineering experience building enterprise applications using Python and .NET - Proven ability to analyze, decompose, and automate requirement-based development using GenAI - Strong experience in building and deploying cloud-based data processing applications using PySpark and Azure Databricks - Hands-on expertise in automating and optimizing cloud infrastructure using Terraform, Bicep, or ARM templates - Deep knowledge of SDLC methodologies and experience leading technical implementations from architecture to production deployment - Proficiency in coding standards, code reviews, source control management, build processes, testing, and operations - Experience integrating AI-based automation for improving code quality, predictive analytics, and data-driven decision-making While experience in the Wealth Management domain is a plus, it is not mandatory for this role. Candidates should be willing to learn and master the domain through on-the-job experience.,