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Mid-level data engineering in a metro location with common skillset increases applicant competition.
Specific cloud data tooling and BI experience makes this moderately transferable across industries.
Explicit 5+ years and many mandatory AWS Glue, PySpark, Iceberg, and QuickSight skills enforce strict filters.
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Own the end-to-end development and maintenance of AWS Glue ETL pipelines ingesting data from multiple SaaS sources into a scalable data lake using Apache Iceberg on S3.
Design, build, and optimize Amazon QuickSight dashboards and datasets for operational and executive reporting, translating business requirements into actionable BI products.
Troubleshoot production data pipelines, implement data quality checks, and maintain technical documentation to ensure reliable data delivery and reporting.
5–10+ years professional experience in data engineering or BI development.
Expertise with AWS Glue (PySpark), Step Functions, S3, Athena, and IAM; strong Python and SQL skills including PySpark and Athena queries.
Experience building ETL pipelines integrating data from REST APIs or SaaS platforms such as Salesforce, JIRA, and Zendesk.
Knowledge of Amazon QuickSight including dataset creation and dashboard development; familiarity with Apache Iceberg or similar lakehouse table formats.
Comfortable owning data engineering tasks independently with minimal supervision, managing priorities and communication across time zones.
Experienced in building robust ETL workflows including orchestration and error handling using AWS Step Functions and AWS event-driven services.
Skilled in translating complex business data needs into reliable, scalable BI solutions using AWS cloud-native tools and lakehouse architecture.