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Tier-1 brand, mid-level generalist title, and popular data engineering skillset increase applicant competition.
Data engineering skills are broadly transferable across industries, so background fit is moderate.
Explicit 3+ years plus mandatory PySpark, AWS, and RPA certifications enforce strict filters.
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Design, develop, and deliver secure, stable, scalable software solutions across multiple technical areas to support business objectives.
Produce architecture and design artifacts for complex applications ensuring design constraints are met by code development.
Develop and optimize scalable data pipelines, analyze complex data sets to identify problems and drive continuous software and system improvements.
3+ years of hands-on experience with RPA (UiPath) and Azure concepts.
Hands-on experience in system design, application development, testing, and production operational stability.
Proficiency in modern programming languages (Python + PySpark or Java/Spark) and database querying (e.g., SQL).
Strong understanding of SDLC, Agile delivery including CI/CD, application resiliency, security, and data engineering experience with ETL/ELT and data lake architecture.
Experienced engineer with a strong background in scalable data pipeline development and data engineering.
Familiar with enterprise AI-assisted software development tools and responsible AI practices within engineering workflows.
Broad technical expertise including AWS data lake services, microservices/containerization (Docker, Kubernetes), and cloud technologies.