As an AWS Lead Data Engineer, you serve as the technical authority and primary architect for the data platform. You are responsible for the high-level design of scalable data systems, guiding the technical roadmap, and mentoring engineering teams to ensure best-of-breed data delivery. You balance hands-on coding in Python/PySpark with strategic decision-making regarding cloud infrastructure and data governance.

Technical Leadership & Strategy Architectural Design: Define the end-to-end architecture for data lakes and warehouses using AWS (Glue, S3, Redshift) and Snowflake. Tech Stack Evolution: Evaluate and implement modern data tools, such as dbt for transformation and Lambda for serverless orchestration. Code Excellence: Establish best practices for SQL optimization, PySpark performance tuning, and CI/CD pipelines. Execution & Delivery Complex Problem Solving: Act as the escalation point for technical roadblocks in high-volume data processing and complex ETL workflows. Resource Management: Oversee the delivery of data projects, ensuring they meet scalability requirements and stay within cloud budget constraints. Security & Governance: Implement robust data security, encryption, and access controls across all AWS environments. Mentorship & Collaboration Team Development: Provide technical guidance and code reviews to senior and mid-level engineers to foster professional growth. Stakeholder Alignment: Translate business requirements into technical specifications, collaborating with Data Scientists and Product Managers.

🔹 Key Skills: • AWS Services – Glue, Redshift, Lambda • Data pipeline development using Apache Spark & big data technologies • Strong programming in Python, Java, and SQL • ETL design, optimization, and cloud infrastructure on AWS • Good communication and stakeholder management skills • Snowflake & DBT experience is a plus 🔹 Qualifications: • B.Tech / B.E / M.Tech / MCA from a reputed university • Computer Science background preferred