Senior Software Engineer - Python with Snowflake
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Data-driven signals on your job's competitivenessMedium — strong employer brand and generic senior title attract applicants, Snowflake specialization reduces the pool.
Medium — core data engineering skills transfer across industries, but Snowflake/dbt/cloud specialization narrows ideal fit.
High — explicit 6–10 years plus mandatory Snowflake, Python, dbt, cloud, IaC and DevSecOps requirements.
Job Description
Structured overview of role & requirementsAbout This Role
Design, develop, and optimize scalable ELT/ETL pipelines using Python, SQL, and Snowflake components including Tasks, Streams, Pipes, and External Tables.
Architect and manage Snowflake environments while optimizing compute costs and query performance using clustering and micro-partitioning.
Lead design and code reviews, mentor junior engineers, and collaborate with Product, Data Science, and Business teams to deliver data-driven solutions.
Minimum Requirements
6–10 years of professional experience in Data Engineering.
Strong expertise in Python including pandas, asyncio, OOP, typing, packaging, pytest.
Hands-on experience with Snowflake at enterprise scale and advanced SQL skills including query performance tuning.
Experience with Cloud platforms (AWS, Azure, or GCP), CI/CD pipelines (GitHub Actions/Azure DevOps/GitLab CI), and Infrastructure-as-Code tools like Terraform or Azure Bicep.
Ideal Candidate Profile
Proven ability to architect and optimize enterprise-scale Snowflake environments integrating with cloud-native compute/storage (especially Azure).
Experience leading teams or mentoring in a senior technical role, driving engineering excellence and platform roadmap discussions.
Strong background in modern data warehousing design patterns (Kimball, Data Vault) and implementation of DevSecOps practices in data engineering workflows.