Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit www.blend360.com.
Job DescriptionWe are looking for a Data Engineering Manager to lead the team responsible for building the data and ML engineering foundation for a growing Machine Learning organization.
This is a technical leadership role combining people leadership, cloud architecture, MLOps, and delivery. The Manager will help establish the technical foundation for the client's long-term machine learning strategy while ensuring the team can deliver production-ready solutions from the outset.
A key aspect of the role will be helping establish the foundation for the client's eventual agentic architecture and AI initiatives. The ideal candidate therefore has exposure to agentic architectures or related AI engineering patterns, in addition to strong Data Engineering and MLOps experience.
Key Responsibilities
- Lead, mentor, and develop a team of Data Engineers focused on data and ML infrastructure.
- Define and implement technical approaches for scalable cloud-based MLOps.
- Establish the engineering foundation required to support machine learning models throughout their lifecycle.
- Oversee the development of data pipelines, ML infrastructure, deployment processes, monitoring, automation, and CI/CD.
- Partner closely with Data Science leadership to ensure models can move efficiently from development into production.
- Define engineering standards, best practices, and reusable patterns for ML and data engineering.
- Guide architecture and technical decisions related to cloud data and ML infrastructure.
- Help establish the technical foundation required for future agentic architecture and AI initiatives.
- Evaluate technical approaches and technologies based on scalability, reliability, maintainability, and delivery needs.
- Balance immediate delivery requirements with longer-term platform and architecture investments.
- Provide technical mentorship and guidance to Senior Data Engineers.
- Collaborate with Data Science and other technical teams to understand requirements and translate them into scalable engineering solutions.
- Communicate technical decisions, risks, dependencies, and progress clearly to stakeholders.
- Drive a strong culture of engineering quality, ownership, collaboration, and continuous improvement.
- Significant professional experience in Data Engineering.
- Strong hands-on experience with cloud MLOps.
- Proven experience leading and mentoring Data Engineering teams.
- Strong understanding of cloud data architecture and machine learning infrastructure.
- Experience designing and implementing production-grade MLOps practices.
- Strong understanding of model deployment, monitoring, versioning, automation, and ML lifecycle management.
- Experience making technical and architectural decisions for data and ML platforms.
- Strong engineering fundamentals and ability to engage in technical discussions with senior engineers.
- Strong communication and stakeholder management skills.
- Ability to balance technical strategy with hands-on delivery and team leadership.
Nice to Have
- Professional experience with Google Cloud Platform (GCP).
- Experience with agentic architectures or AI engineering.
- Experience helping organizations establish the technical foundation for future AI initiatives.
- Experience working closely with Data Science teams to operationalize machine learning models.
- Experience defining long-term data and ML platform strategies.
Our Perks and Benefits:
📚 Learning Opportunities:
- Certifications in AWS (we are AWS Partners), Databricks, and Snowflake.
- Access to AI learning paths to stay up to date with the latest technologies.
- Study plans, courses, and additional certifications tailored to your role.
- Access to Udemy Business, offering thousands of courses to boost your technical and soft skills.
- English lessons to support your professional communication.
👩🏫 Mentoring and Development:
Career development plans and mentorship programs to help shape your path.
🎁 Celebrations & Support:
- Special day rewards to celebrate birthdays, work anniversaries, and other personal milestones.
- Company-provided equipment.
⚖️ Flexible working options to help you strike the right balance.
Other benefits may vary according to your location in LATAM. For detailed information regarding the benefits applicable to your specific location, please consult with one of our recruiters.
Skills Required
- Significant professional experience in Data Engineering
- Strong hands-on experience with cloud MLOps
- Proven experience leading and mentoring Data Engineering teams
- Strong understanding of cloud data architecture and machine learning infrastructure
- Experience designing and implementing production-grade MLOps practices
- Strong understanding of model deployment, monitoring, versioning, automation, and ML lifecycle management
- Experience making technical and architectural decisions for data and ML platforms
- Strong engineering fundamentals and ability to engage in technical discussions with senior engineers
- Strong communication and stakeholder management skills
- Ability to balance technical strategy with hands-on delivery and team leadership
- Professional experience with Google Cloud Platform (GCP)
- Experience with agentic architectures or AI engineering
- Experience helping organizations establish the technical foundation for future AI initiatives
- Experience working closely with Data Science teams to operationalize machine learning models
- Experience defining long-term data and ML platform strategies
Blend360 Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Blend360 and has not been reviewed or approved by Blend360.
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Fair & Transparent Compensation — Pay is considered fair-to-good by many, and public salary postings for common data roles indicate competitive packages in numerous markets. Feedback suggests overall company sentiment aligns with acceptable compensation relative to peers in consulting and analytics.
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Flexible Benefits — Flexible and remote/hybrid work arrangements are consistently highlighted in official materials and role descriptions. Feedback suggests flexibility is a meaningful part of the total rewards experience.
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Retirement Support — A 401(k) with company match is part of the core package. Feedback suggests retirement offerings are standard and contribute to a complete benefits set.
Blend360 Insights
What We Do
Our Vision is to build a company of world-class people that helps our clients optimize business performance through data, technology and analytics. Blend360 has two divisions: Data Science Solutions: We work at the intersection of data, technology and analytics. Talent Solutions: We live and breathe the digital and talent marketplace.
