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Mid-level generalist data engineer (2–4 years) with common stack attracts moderate competition.
Core data engineering skills are highly transferable across industries.
Explicit 2–4 year requirement plus mandatory Snowflake, AWS, Python, SQL, and Airflow.
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Design, develop, and maintain scalable ETL/ELT pipelines for large-scale data ingestion, transformation, and loading.
Build and optimize data warehouse solutions in Snowflake, including implementing RBAC, ABAC, Snowpipes, and Dynamic Tables for analytics and reporting.
Manage and orchestrate workflows and data integration using AWS services (Glue, Lambda, SNS, S3), Kafka, Airflow, and Python automation scripts, ensuring data quality and operational reliability.
2–4 years of experience in software development, data engineering, or related roles.
Proficiency with ETL/ELT processes, data warehousing concepts, and experience building data pipelines with SQL and Python.
Hands-on experience with AWS services: Glue, Lambda, SNS, S3 and workflow orchestration tools like Airflow.
Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field.
Experienced in building and optimizing cloud-based data integration solutions, particularly using Snowflake and AWS services.
Comfortable working in fast-paced, agile environments with a team-first approach and strong problem-solving skills.
Able to manage end-to-end data pipeline lifecycle including design, implementation, troubleshooting, and documentation to support scalable analytics platforms.