Data Engineer Manager
Summary
Lead a team to design and maintain scalable data pipelines, warehouses, and analytics platforms, balancing cost, speed, and quality for production systems.
- Design and optimize scalable, cost-efficient end-to-end data architectures.
- Improve data delivery speed and engineering efficiency year over year.
- Build reusable frameworks, templates, and automation to reduce repetitive work.
- Develop and maintain reliable data pipelines, real-time systems, and analytics platforms.
- Deliver production-ready solutions under tight timelines without compromising quality.
- Resolve complex production issues independently and implement long-term fixes.
- Evaluate new tools, frameworks, and architectures with clear cost-benefit analysis.
- Proactively reduce technical debt and prevent recurring scalability issues.
- Make and communicate technical trade-offs (e.g., batch vs. streaming, build vs. buy).
- Apply advanced data or analytics concepts in production systems.
- Act as a technical authority through code reviews, design reviews, and best practices.
- Collaborate with stakeholders and translate technical decisions into business impact.
Qualifications
- Bachelor's degree in Computer Science, Engineering, Data Science, or a related field.
- Minimum 3 years of experience in data engineering or related roles.
- Strong experience building and operating production-scale data systems.
- Hands-on experience with data pipelines, data warehouses/lakes, and cloud platforms.
- Solid understanding of batch and streaming data processing.
- Experience supporting analytics, machine learning, or AI data workflows.
- Strong problem-solving skills in complex production environments.
- Strong judgment in balancing speed, quality, cost, and scalability.
- Clear communication skills with both technical and non-technical stakeholders.