Back
Tier-1 brand, metro location, and broad mid-senior data engineering skillset drive high competition.
Modern Airflow/Snowflake/dbt/Python data engineering skills are broadly transferable across industries.
Mandatory 8+ years and specific Snowflake, Airflow, dbt, Python expertise enforce strict screening.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, build, and optimize enterprise-scale data platform capabilities including data pipelines, metadata-driven processing, data quality, governance, and workflow orchestration using Python, SQL, Airflow, Snowflake, and cloud technologies.
Support production operations with L2/L3 support, perform root cause analysis, and drive continuous improvements in platform performance, scalability, reliability, and automation.
Lead technical design, collaborate cross-functionally, mentor junior engineers, conduct POCs for emerging technologies, and contribute to platform modernization and engineering best practices.
Bachelor’s degree in Computer Science, Information Systems, or related technical field.
8+ years of experience in Data Engineering or related software engineering roles.
Hands-on expertise with Apache Airflow, Python, Snowflake or equivalent cloud-native analytical data platforms, SQL query optimization, and dbt for data transformation and modeling.
Experience with cloud-native data platforms on Azure, AWS, or GCP and exposure to streaming technologies (e.g., Snowpipe, Kafka) and containerization (Docker, Kubernetes).
Senior-level data engineering professional capable of driving cloud-native platform development and operational excellence at enterprise scale.
Strong technical leadership with experience mentoring teams, leading proofs of concept, and influencing architecture and platform decisions in cross-functional environments.
Familiarity with advanced data engineering concepts including metadata management, data governance, AI-assisted engineering tools, and modern DevOps practices in Agile settings.