Data Engineer - INTL India at Insight Global
Seattle, WA
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Jul 21, 2026
Seattle, WA
Jul 23, 2026
What this job asks for AI summary
A data engineering role focused on building and maintaining ETL pipelines and data warehouse solutions using Snowflake and Azure cloud services. Day-to-day work involves designing data flows for both structured and unstructured data, integrating real-time streaming technologies, and writing optimized SQL for transformation and analysis. Suited to engineers with a solid background in data pipeline development, cloud data platforms, and programming in Python, Scala, or Java.
Mid level · 3+ years
“or” means any one of them counts — you don't need all of them.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Rare in this occupation — lead with these, and say what you built with them.
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $106,921 (middle half $81,361–$138,439).
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (7)
No work location or metro area is specified in the posting; the CBSA is left blank. The role is posted through Insight Global (a staffing firm), so the actual client location is unknown.
The 3–5 year experience range is treated as a 3-year minimum for the overall years minimum.
SOC classification is a judgment call: the role centers on designing and building data pipelines and warehouse solutions (15-1243 Database Architects), but the heavy emphasis on Python/Scala/Java programming and pipeline code also makes 15-1252 Software Developers a plausible fit.
Apache Airflow is listed under the same bullet as Azure Data Factory for orchestration; both are captured, with Airflow as the primary and ADF as an alternative since ADF already appears as a required Azure Data Service.
Hadoop, Spark, and Delta Lake appear under 'Big Data technologies' in the requirements section but are framed as a single grouped requirement; all three are captured, with Spark and Delta Lake also listed separately as preferred given the phrasing.
Machine learning pipelines and MLOps are explicitly called 'a plus' and are omitted from the skills list as they name no specific concrete tool.
Remote status is unspecified; defaulting to false.
Read the full posting
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