TL;DR
Associate Director (Data Engineering): Leading the design, implementation, and management of advanced data pipelines and analytics platforms using Databricks and Azure services with an accent on cloud infrastructure optimization and CI/CD excellence. Focus on driving engineering standards, mentoring technical teams, and ensuring high availability for complex data-intensive applications.
Location: Hybrid work environment.
Company
hirify.global is a global research and advisory firm providing actionable, objective business and technology insights to enterprise leaders.
What you will do
- Lead the administration and optimization of core Azure services including AKS, Azure Storage, and Azure Data Factory.
- Drive platform support for Databricks environments by implementing best practices in monitoring and troubleshooting.
- Implement and optimize CI/CD pipelines to ensure rapid, reliable deployment cycles for data engineering projects.
- Collaborate with cross-functional teams to integrate data applications with enterprise infrastructure and security services.
- Define and enforce robust data engineering standards, security protocols, and compliance measures.
- Mentor technical teams and foster a culture of continuous learning and innovation.
Requirements
- Bachelor’s or master’s degree in Computer Science, Engineering, or a related field.
- 8-12 years of experience in data platform management or cloud engineering.
- At least 2 years of experience in coaching and mentoring technical teams.
- Expertise in Azure cloud services and Databricks.
- Solid understanding of CI/CD, cloud security, and disaster recovery solutions.
- Proficiency in Python, Scala, and SQL.
Culture & Benefits
- Hybrid work model offering flexibility between virtual work and collaborative office time.
- Highly competitive compensation with rewards for top performance.
- Inclusion-focused culture composed of diverse global teams.
- Professional growth opportunities within a large-scale, industry-leading organization.
- Focus on innovation and outcome-driven results.
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