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Mid-level, common Data Engineer title but specialized Azure/PySpark platform needs moderate applicant competition.
Azure and platform-specific requirements increase domain specificity, though core data engineering skills remain transferable.
Explicit 5+ years and required Azure, PySpark, and production data platform experience create high shortlisting strictness.
Build and maintain batch and streaming data pipelines from source systems to curated datasets used by analysts, data scientists, and application teams.
Own pipeline reliability including monitoring, on-call incident response, root cause analysis, and performance tuning with explicit cost considerations.
Design schemas and data models for analytics; implement data quality checks and compliance controls; maintain documentation including data dictionaries and lineage.
5+ years of experience building production data pipelines.
Proficient in Python development for data engineering with testing and CI/CD experience.
Strong SQL and PySpark skills including performance tuning and troubleshooting.
Hands-on experience with Azure data platform components: Data Factory, Databricks, Synapse/Fabric, and ADLS.
Experienced in data modeling fundamentals such as normalization, star schemas, and slowly changing dimensions, comfortable balancing query performance and maintainability.
Demonstrates engineering discipline with version control, code review, automated testing, and infrastructure as code practices.
Consultative approach with customers in technical environments; capable of translating complex pipeline issues to non-technical stakeholders for impact analysis.