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Mid-level data engineer role, metro location, and broad skillset requirements drive high candidate competition.
Core data engineering skills like SQL, ETL, Python, Snowflake and dbt are highly transferable across industries.
Explicit 5+ years plus mandatory cloud, dbt, Snowflake, SQL and streaming experience creates moderately strict shortlisting.
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Build and maintain high-performance, fault-tolerant, scalable data platform systems supporting enterprise-grade analytical products.
Contribute to Semantic Layer and Data Sharing implementations, enabling governed data access and standardized business metrics using tools like dbt MetricFlow.
Drive performance engineering by tuning distributed storage and query performance for large datasets; own operational reliability including on-call incident response.
Bachelor’s degree in Computer Science or a related field.
5+ years of hands-on experience in Software Engineering or Data Engineering within high-traffic, highly available production environments.
Proficiency with cloud data warehouses (e.g., Snowflake), SQL, and data transformation tools like dbt.
Experience with programming languages such as Python, Spark, Java, or Scala, plus prior on-call and CI/CD adherence experience.
Experienced in Semantic Modeling concepts with practical exposure to performance troubleshooting and query optimization.
Skilled in managing large-scale data platforms and building clean semantic models supporting enterprise data governance.
Comfortable working in high-scale, production-critical environments with a focus on operational ownership and cross-team collaboration.