Snowflake Data Engineer - Ads
Apple · Software and Services
Apply ↗Skills
["AWS""Airflow""CI/CD""Go""Jenkins""Linux""Python""RAG""SQL""Snowflake""Terraform""dbt"]Technology stack
{"programming_languages": ["Python""Go""SQL"]"frameworks": ["Airflow"]"databases": ["Snowflake"]"cloud": ["AWS"]"infrastructure": ["Terraform""CI/CD""Jenkins""Linux"]"data_tools": ["dbt"]"ai_ml": ["RAG"]"other_tools": []}Description
In this role, you’ll work primarily with Snowflake on AWS, alongside open-format lakehouse technologies like Apache Iceberg, and grow your skills in cloud infrastructure, CI/CD, and automation. This is a hands-on role for someone who enjoys scripting away toil, learning cloud-native tooling, and partnering with engineering teams. You’ll be mentored by senior engineers while taking increasing ownership of real production data platforms.
Requirements
["5-8 years of experience in IT / software / database / data engineering", "Hands-on experience with Snowflake (or another cloud data warehouse such as Redshift, BigQuery, or Databricks with a clear path to Snowflake); solid understanding of core database and data-warehousing concepts.", "Strong working knowledge of SQL and fundamentals of query tuning and execution plans (e.g., reading query profiles, clustering/pruning basics, warehouse sizing, and Account Usage / Information Schema views).", "Experience with at least one major cloud provider, ideally AWS, and its core services (compute, managed storage such as S3, networking, security groups, VPC, PrivateLink, KMS, IAM).", "Proficiency in Python for scripting and automation (Snowpark Python a plus).", "Exposure to CI/CD and DevOps tooling (e.g., Git, Jenkins/GitLab CI/GitHub Actions, and infrastructure-as-code such as Terraform or CloudFormation, Helm, ArgoCD, ACK).", "Exposure to open-format lakehouse technologies such as Apache Iceberg (a plus).", "A demonstrated bias toward automation — a track record of scripting or automating repetitive work.", "Working knowledge of Linux/Unix fundamentals.", "Strong analytical and problem-solving skills, eagerness to learn, and good communication and collaboration skills.", "Preferred: Exposure to GenAI application building, MCP and prompt engineering", "Preferred: Experience with Streamlit, Snowpark Python / Snowpark pandas for in-database transformations.", "Preferred: Hands-on with Apache Iceberg tables, external volumes, and open-format lakehouse patterns.", "Preferred: Familiarity with AWS networking and connectivity — PrivateLink, VPC peering, gateways, and load balancing.", "Preferred: Experience building and orchestrating data pipelines (e.g., dbt, Airflow, Dagster, or Snowflake Tasks & Streams).", "Preferred: Exposure to observability and monitoring stacks (e.g., Datadog, Prometheus/Grafana, Splunk)."]
Roles & responsibilities
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