Key Responsibilities
Lead the design, development, and deployment of scalable data pipelines using AWS Glue, Apache Spark, and PySpark.
Design and implement up-to-date Lakehouse architecture leveraging Apache Iceberg.
Build and maintain robust ETL/ELT pipelines using Python and dbt.
Develop and manage workflow orchestration using Apache Airflow or AWS-native orchestration services.
Design and optimize data models, ETL processes, and SQL queries for Amazon Redshift and analytical workloads.
Establish best practices for CI/CD, source control using GitHub, and automated testing.
Collaborate with business stakeholders, architects, and cross-functional teams to translate business requirements into technical solutions.
Provide technical leadership, mentor engineering teams, and drive code quality, performance optimization, and architectural best practices.
Ensure data quality, governance, reliability, and operational excellence across data platforms.





Skills Required

Skills and Experience
8+ years of experience in Data Engineering, with experience leading technical teams.
Solid hands-on expertise in AWS Glue, Apache Spark, PySpark, and Python.
Experience with dbt for data transformation, testing, and documentation.
Hands-on knowledge of Apache Iceberg and Lakehouse architecture.
Experience with Apache Airflow for workflow orchestration.
Solid expertise in Amazon Redshift, SQL optimization, and performance tuning.
Experience with GitHub, GitHub Actions, Jenkins, AWS CodePipeline, or similar CI/CD tools.
Solid understanding of data modeling, partitioning strategies, and performance optimization.
Excellent problem-solving, communication, and stakeholder management skills.

📌 Lead Data Engineer Bengaluru (India)
🏢 Flexing It®
📍 India