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
We are seeking a Data Engineering Manager to contribute to our next level of growth and expansion.
Job DescriptionWhat is this position about?
We're looking for a Data Engineering Manager to lead the design and delivery of complex data pipelines and integration solutions that power critical business workflows for our clients. You will own end-to-end data engineering initiatives from discovery and POC development through production deployment and optimization, while building and scaling a high-performing engineering team.
Your work will span platform-agnostic data architecture, high-volume record matching, deduplication, and near-real-time delivery systems. You will guide teams through complex data modeling decisions, performance optimization, and integration design, helping clients understand the tradeoffs between batch and real-time approaches, API-driven versus clean-room workflows, and infrastructure options.
What You Will Do
- Design and implement data ingestion architectures on Snowflake.
- Lead data engineering projects end to end—from discovery and requirements through POC validation to production deployment—while building and mentoring a high-performing engineering team
- Design platform-agnostic data pipelines and schemas that scale reliably at high volume, including complex data modeling for submit-grade-return workflows, identity graphs, record matching, and deduplication systems
- Own discovery pipeline initiatives and functional POCs, implementing high-volume matching logic and conducting comprehensive performance, load, and scale testing to validate architectural decisions
- Assess and optimize latency, throughput, connection stability, and integration options including APIs, batch delivery, and governed clean-room workflows
- Define data integration strategies and communicate tradeoffs clearly to clients, guiding infrastructure and tool selection based on performance requirements and business constraints
- Lead partner-facing API design and integration strategy, architecting batch versus real-time decisions and designing governed clean-room solutions for partner data exchange
- Establish technical standards for schema design, SQL quality, pipeline development, and production readiness across the team through reviews, mentoring, and knowledge sharing
What We Are Looking For
- 7+ years of hands-on data engineering experience, with proven expertise in building and deploying high-volume data pipelines in production
- 2+ years of direct team leadership or technical management experience
- Advanced SQL proficiency with deep expertise in schema and data-model design, complex joins, and performance optimization
- Proven track record with high-volume record matching, deduplication, and identity resolution systems
- Strong experience with platform-agnostic data engineering—ability to assess and implement solutions across Snowflake, Databricks, BigQuery, Redshift, and other platforms
- Demonstrated expertise in pipeline development, performance testing, latency and throughput analysis
- Experience designing and implementing batch and real-time data integration workflows
- Knowledge of governed clean-room solutions and data governance practices
- Solid understanding of API design, microservices patterns, and event-driven architectures
- A clear communicator equally comfortable with engineering teams and senior stakeholders
- Strong hiring and team-building instincts with proven mentoring experience
What about languages?
- English: Advanced (required for effective communication with global teams and client leadership).
How much experience must I have?
7+ years of hands-on data engineering experience in production environments, with 2+ years of direct team leadership or technical management responsibility.
Additional Information
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.
👨🏽💻 Travel opportunities to attend industry conferences and meet clients.
👩🏫 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
- 7+ years of hands-on data engineering experience building and deploying high-volume production data pipelines
- 2+ years of direct team leadership or technical management experience
- Advanced SQL proficiency, including schema design, data modeling, complex joins, and performance optimization
- Experience with high-volume record matching, deduplication, and identity resolution systems
- Experience implementing platform-agnostic solutions across Snowflake, Databricks, BigQuery, Redshift, or similar platforms
- Expertise in pipeline development, performance testing, latency analysis, and throughput analysis
- Experience designing and implementing batch and real-time data integration workflows
- Knowledge of governed clean-room solutions and data governance practices
- Understanding of API design, microservices patterns, and event-driven architectures
- Clear communication skills with engineering teams and senior stakeholders
- Hiring, team-building, and mentoring experience
- Advanced English proficiency
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.
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