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Tier-1 employer and metro locations increase candidate density, balanced by seniority and specialized dbt/Snowflake skillset.
Specialized dbt/Snowflake data engineering skills are transferable, though enterprise-scale experience increases domain specificity.
Multiple mandatory techs (dbt, Snowflake, SQL), senior-level data engineering and degree make filters highly strict.
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Design and build scalable data models and manage data ingestion using tools like SnapLogic and Confluent Cloud.
Shape and enforce data architecture standards including data models, metadata, and security across environments.
Lead technical protocols in a DataOps setting, mentor team members, and consult cross-functional teams on complex data modeling and warehousing challenges.
University degree in IT, Engineering, Economics, or a related quantitative field.
Extensive experience in data engineering and data warehousing with proven skills in building and maintaining complex data models.
Expert-level SQL programming and deep proficiency with dbt; strong experience with Snowflake cloud data warehouse.
Experience with data ingestion tools such as SnapLogic or Confluent Cloud and at least one general-purpose programming language, preferably Python.
Strong background in cloud data warehouses, especially Snowflake, with experience across major cloud platforms like AWS, GCP, or Azure.
Experience working in agile software development environments, driving DataOps practices and continuous improvement.
Demonstrated technical leadership and effective collaboration within autonomous, cross-functional agile teams.