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Tier-1 brand, metro location, popular mid-level data role with broad stack and 3+ years requirement.
Core data engineering skills are transferable across industries, though SaaS-specific tooling adds moderate domain specificity.
Explicit 3+ years plus mandatory Spark, SQL, Airflow and cloud experience increases filtering.
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Develop and maintain automated data pipelines supporting analytics datasets and business functions.
Collaborate with senior engineers and cross-functional teams to implement data transformations, modeling, and quality checks.
Leverage AI-assisted tools and explore AI/LLM-powered workflows to automate data tasks and enhance team productivity.
3+ years of experience building and maintaining data warehousing and analytics solutions.
Proficiency in distributed data processing frameworks (e.g., Spark, Hive, Iceberg) and SQL for complex analytical queries.
Experience with data pipeline development using Spark and orchestration tools like Airflow.
Working knowledge of Python, Java, or Scala for data transformations; cloud platform experience (preferably AWS).
Experience working collaboratively with data scientists, analysts, and product teams to align data solutions with business needs.
Demonstrated ability to improve data reliability and documentation under guidance, showing capacity for growth.
Technical familiarity with AI agents, LLM-powered applications, and AI-assisted development tools as applied to data engineering workflows.