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Remote, mid-level data engineer role with a popular title attracts many qualified applicants.
Cloud data engineering skills are transferable but SaaS/insurance metrics and US-hours raise domain specificity.
Explicit 5+ years, specific Google Cloud data stack, SQL expertise, and US-hours requirement raise shortlisting strictness.
Build, optimize, and maintain ETL/ELT data pipelines using Google Cloud technologies (BigQuery, Dataflow, Cloud Composer) supporting business intelligence and reporting.
Develop and refine data models (Star/Snowflake schema) and high-performance SQL scripts handling complex data transformations with focus on accuracy and efficiency.
Collaborate with stakeholders to translate business metrics into technical specifications and proactively resolve data pipeline bottlenecks optimizing query speed and cloud costs.
5+ years of professional experience in data engineering with cloud-based data warehouses.
Strong expertise in Google Cloud ecosystem tools including BigQuery, Cloud Storage, and IAM.
Expert-level SQL skills including window functions, recursive CTEs, and query plan analysis.
Ability to work US Eastern Standard Time core hours overlap (7:30pm-11:30pm IST) with a dedicated quiet workspace and reliable internet connection (50 Mbps minimum).
Experienced in data engineering roles within SaaS and/or insurance domains with familiarity of related metrics (e.g., MRR, Churn, Premiums, Claims).
Proven track record collaborating with US-based business teams in publicly traded companies or similar environment.
Strong focus on data integrity and clean code with ability to balance new development and legacy troubleshooting in a team-based setting.