Lead data engineer responsible for designing, building, and operating production data pipelines using Python, Airflow, Snowflake, DBT, and AWS. Automate ETL, implement CI/CD, testing and DBT validations, troubleshoot pipelines, and work client-facing to deliver reliable data solutions.
Job Description Job Descriptions Data Engineer Must have below: 10+ Years Experience Great Communicator/Client Facing/ Attention to detail Individual Contributor and ability to work as a team. 100% Hands on in the mentioned skills Programming Skills: Python and Data Pipelines: Advanced Proficiency in Python concepts like Code Structures, Modules, Packages, Class, SubClass, Inheritance, Multi-Threading and Functional Programming. Experience in developing reusable Python packages for internal or public usage Ability to write automating ETL processes and scheduling jobs like Airflow DAG. Ability to track job pipeline runs to reprocess error records Ability to orchestration different pipelines to run in sequence or parallel. Troubleshoot data pipeline errors and fix issues Export or Import data to/from various formats like CSV, JSON, XML etc preferably from S3 or other cloud storage. Experience in using AI IDE tool SQL: Advanced SQL skills, including complex joins, CTE's and subqueries Experience in optimizing SQL queries for performance and optimization in data warehouse technologies preferably Snowflake Testing and documentation: Proficiency in Python unit, integration and system test. Proficiency in implementing DBT tests for data validation and quality checks Code Generation: Experience in generating code using configurations using python and jinja templates Version control: Experience in GitHub, including implementing CI/CD process from scratch AWS Expertise: Data Storage solutions: In depth understanding of AWS S3 for data storage, ECS, IAM including best practices for organization and security
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