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Metro mid-level analytics role with broad cloud/data requirements and moderate brand, creating high candidate competition.
Core cloud data engineering skills are highly transferable across industries, so background fit sensitivity is low.
Mandates deep AWS/data platform expertise, architecture knowledge, CI/CD familiarity and team leadership, so strict shortlisting.
Design, develop, and maintain AWS cloud-based low-code data pipelines and data warehouse/lake solutions to enable efficient reporting and data analysis.
Lead end-to-end data engineering including data modeling, automation, performance optimization, and governance for scalable business insights.
Collaborate with stakeholders and cross-functional teams to define data requirements, drive data initiatives and communicate data strategy progress effectively.
Extensive experience with AWS cloud data platforms and delivering large-scale data engineering solutions in a cloud environment.
Hands-on expertise in ETL/ELT processes, data warehousing, data lakes, data governance, and modern data architectures (Data Lake, Warehouse, Lakehouse, Mesh).
Experience managing and leading a small team of data engineers.
Work Experience Required: Not explicitly mentioned in the JD.
Proven ability to manage full lifecycle data pipeline implementations with a strong focus on automation and performance in an Agile environment (Scrum/Kanban).
Strong communicator capable of bridging technical and business stakeholders, with experience in influencing data-driven initiatives.
Experienced in architecting cost-effective, scalable data solutions with knowledge of CI/CD practices and data quality monitoring.