Back
Metro Bangalore, popular Data Engineer title and mid experience increase competition despite niche SQL/ETL requirements.
Core data engineering skills are transferable, but SQL AST and Snowflake expertise increase domain specificity.
Explicit 2–4 years plus mandatory Python, SQL, SQL AST, Snowflake and Airflow/dbt skills.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design and build large-scale, high-performance data pipelines processing petabyte-scale data and supporting 100K+ daily jobs.
Develop cloud-native data infrastructure primarily on AWS using technologies like Kubernetes and Airflow for scalable resource management.
Create an intelligent SQL ecosystem including query optimization, dynamic ETL pipeline generation, and column-level data lineage using SQL AST and parsers.
2-4 years experience in data engineering with focus on scalable data pipelines and systems.
Strong proficiency in Python and SQL with expertise in SQL query profiling, optimization, and performance tuning, preferably on Snowflake.
Experience with SQL Abstract Syntax Tree (AST) and SQL parsers (e.g., sqlglot) for lineage and dynamic ETL workflows.
Experience building data pipelines using Airflow or dbt; knowledge of AWS and Kubernetes is optional but beneficial.
Experienced engineer comfortable working at scale on cloud-native data infrastructure with petabyte data volumes.
Deep technical expertise in SQL internals and optimizing complex queries to improve performance in data systems.
Experienced in integrating AI into data engineering workflows and building solutions that have large-scale user impact in enterprise environments.