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Mid-level data engineer role in Bangalore with generalist requirements and startup brand attracts high applicant competition.
Core data engineering skills are transferable, though SQL AST and Snowflake expertise increase specificity.
Explicit 2-4 year requirement plus strong SQL/AST, Snowflake and pipeline skills make filtering strict.
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Build and maintain highly performant, large-scale data pipelines and infrastructure handling petabyte-scale data.
Design and implement cloud-native data infrastructure on AWS with Kubernetes and Airflow for scalable resource management.
Develop an intelligent SQL ecosystem focusing on query optimization, dynamic pipeline generation, and column-level data lineage using SQL AST and parsers.
2-4 years of experience in data engineering with expertise in scalable data pipelines and systems.
Strong proficiency in Python and SQL with extensive experience in SQL query profiling, optimization, and performance tuning (preferably Snowflake).
Experience working with SQL Abstract Syntax Tree (AST) and SQL parsers like sqlglot for lineage and dynamic ETL generation.
Experience building data pipelines using Airflow or dbt; understanding of AWS cloud platform preferred but optional.
Experienced in operating at petabyte-scale data infrastructure with a strong focus on performance and scalability.
Technically skilled in SQL AST analysis and parser-based data lineage to innovate in query optimization and pipeline adaptability.
Comfortable contributing to open-source projects and advancing AI-powered data engineering technologies in a high-impact, global environment.