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Senior role reduces applicants, but popular data-engineering stack and enterprise brand keep competition medium.
Core data engineering skills are transferable across industries, but senior enterprise experience increases domain specificity.
Exact 13–14 years requirement and mandatory dbt, Snowflake, Airflow, Python, GitLab CI/CD make filters strict.
Design, develop, and maintain scalable ETL/ELT pipelines and enterprise data platforms using dbt, Snowflake, Airflow, SQL, and Python.
Implement and optimize complex data transformations, data models, and data warehouse solutions to support business use cases.
Build and automate deployment pipelines using GitLab CI/CD; support AI/ML initiatives by creating feature-ready datasets and scalable data workflows.
13–14 years of professional experience in Data Engineering, ETL, Data Warehousing, or related domains.
Strong hands-on expertise with dbt, Snowflake, Apache Airflow, Python, SQL, and GitLab CI/CD including jobs and runners.
Bachelor's or Master's degree in Computer Science, Engineering, IT, Data Science, or related field.
Experience supporting AI/ML initiatives including data preparation, feature engineering, and MLOps concepts.
Experienced in enterprise-scale data engineering with proven ability to design and optimize complex data pipelines and models.
Proficient in building automated CI/CD workflows for data deployment and maintaining data performance, observability, and governance.
Able to collaborate cross-functionally with Data Scientists, Analysts, and Architects while mentoring technical teams and influencing data engineering best practices.