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Popular generalist title and broad data skill requirements increase applicant density despite weaker employer brand.
Data engineering skills are transferable across industries but still require domain-specific tooling and pipeline experience.
No explicit years or certifications but domain-specific data skills and possible leadership raise screening rigor to medium.
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Build and maintain data architectures including pipelines, data warehouses, and data lakes ensuring data accuracy, accessibility, and security.
Design and implement data processing and analysis algorithms suitable for complex data volumes and collaborate with data scientists to deploy machine learning models.
Lead or supervise a team by guiding professional development, allocating work, coordinating resources, and taking ownership for operational results and risk management.
Technical expertise in data engineering including pipeline and data warehouse/lake development.
Experience with data processing and analysis algorithm development and deployment.
Work Experience Required: Not explicitly mentioned in the JD.
Ability to lead or collaborate with cross-functional teams and manage risk and compliance in data operations.
Expert in building scalable and secure data architectures handling high data volume and velocity.
Capable of leading teams or acting as a technical advisor with influence over data and analytics operations.
Experienced in collaborating across business units and integrating technical solutions with business objectives.