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Mid-level generalist data role, metro location, and recognizable enterprise brand increase applicant density.
Data engineering skills are moderately transferable across industries but require platform-specific cloud and tooling experience.
Explicit 2-4 years plus specific Azure/Databricks/Spark/SQL requirements imply moderate strictness.
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Architect, build, and scale enterprise data platforms and cloud-native ELT/ETL pipelines to enable advanced analytics and decision-making.
Ensure reliable data availability with strong governance, quality standards, and high performance across data warehousing, Lakehouse, and BI environments.
Collaborate cross-functionally to translate business requirements into scalable data solutions while optimizing pipeline and query performance.
2-4+ years of experience in data engineering, BI/analytics engineering, or data architecture.
Proficiency in SQL, data modeling, and performance tuning.
Hands-on experience with cloud data platforms, preferably Azure Synapse and Azure Databricks.
Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or related field.
Experienced in designing and maintaining scalable ETL/ELT pipelines and large-scale data processing using Spark or similar technologies.
Strong background in cloud-based data architectures, especially Microsoft Azure ecosystem components.
Ability to support data quality, governance, and compliance standards while enabling BI and AI/ML data needs.