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Generic SDE title, mid-level experience, metro hiring, and broad data stack increase candidate competition.
Core data engineering skills like SQL, Python, ETL and Snowflake are highly transferable across industries.
Explicit 2–4 year requirement plus mandatory Snowflake/AWS/Python/SQL skills make filters stringent.
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Design, develop, and maintain scalable ETL/ELT pipelines and data warehouse solutions on Snowflake for analytics and reporting.
Build, optimize, and manage cloud data engineering workflows using AWS services (Glue, Lambda, SNS, S3), Kafka, and orchestration tools like Airflow or Step Functions.
Ensure data quality, reliability, and troubleshoot production issues in data pipelines while collaborating with cross-functional teams for requirements and implementation.
2–4 years of hands-on experience in software development or data engineering with ETL/ELT and data warehousing.
Proficiency in SQL, Python, and strong familiarity with AWS Glue, Lambda, SNS, S3, Snowflake (RBAC, ABAC, Snowpipes, Dynamic Tables), Kafka (basic-medium), and Airflow.
Bachelor’s degree in Computer Science, Information Technology, Engineering, or related field.
Work Experience Required: 2–4 years relevant experience.
Operates effectively in fast-paced, collaborative engineering environments handling cloud-based data integration and scalable data pipelines.
Strong foundation in both development and operational aspects of large-scale data workflows and cloud platforms, especially AWS and Snowflake.
Demonstrates analytical and problem-solving skills focused on data platform performance, reliability, and automation.