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?
- Design, build, and maintain semantic layers and KPI models using Databricks Metric Views to underpin governed executive scorecards and AI-powered analytical solutions.
- Work directly with business owners to define, validate, and translate KPI requirements into reusable data models and business logic.
- Profile source data quality, ownership, data grain, and reconciliation requirements across enterprise source systems.
- Design and implement data integration and transformation pipelines that prepare enterprise data for AI-generated narratives and conversational analytics.
- Define conformed dimensions and market-specific data variations to support multi-market reporting and analytics.
- Collaborate closely with Business Analysts and AI Engineers to align KPI definitions with underlying data structures and business ontologies.
- Establish and enforce data quality, validation, and monitoring frameworks across all data assets feeding analytical applications.
- Implement security, access control, and governance practices aligned with platform and AI governance standards.
- Lead technical documentation and knowledge transfer initiatives at the conclusion of each delivery phase.
- Support production readiness assessments and oversee the deployment of solutions to production environments.
- 7+ years of experience in Data Engineering, with demonstrated expertise in semantic layer and KPI/metric modeling.
- Strong hands-on experience building and maintaining Databricks Metric Views or equivalent semantic/metric layer tooling.
- Advanced proficiency in SQL and Python for data processing, transformation, and pipeline development.
- Solid understanding of cloud data platforms, specifically Azure Databricks, and modern ELT/ETL tooling.
- Demonstrated expertise in data modeling techniques, conformed dimensions, and Medallion-style architectures.
- Experience profiling data quality, lineage, and reconciliation across multiple source systems.
- Comfort working directly with business stakeholders to gather, validate, and implement KPI requirements.
- Understanding of business ontology and semantic modeling concepts.
- Proficiency with Git version control and collaborative development practices.
- Knowledge of how data engineering supports AI/LLM-based analytics, including feature preparation for narrative generation and conversational analytics.
- Experience with FMCG/CPG or retail data ecosystems (POS, SKU, category, and market performance datasets) is a plus.
What about languages?
English: Advanced (required for effective communication with global teams)
How much experience must I have?
7+ years of experience in Data Engineering or related disciplines such as Data Architecture or Analytics Engineering, with demonstrated expertise in semantic modeling, KPI development, and multi-source data integration.
Additional InformationOur Perks and Benefits:
🏥 Health and Well-being:
- At-home medical assistance via EMI (or similar provider) through Asobursatil, available for all employees from AllStar to Analyst level.
- Private healthcare plans for Lead-level roles and above.
🎉 Celebrations and Recognitions:
- Christmas kit delivered to all employees.
- 1 day off for academic graduation.
- Family Day: 1 day off every semester (must be taken within the same semester).
💰 Financial Health and Savings (Work Together, Get Together Program):
- Savings incentive program via Asobursatil:
- Year 1: Blend contributes 50% of your monthly savings.
- Year 2: Blend contributes 100% of your monthly savings.
- Year 3+: Blend contributes 150% of your monthly savings.
- Savings can be withdrawn in July and December.
📚 Educational Loans and Subsidies:
- Forgivable education loans subject to committee approval and budget availability.
- Requirements: 1+ year at Blend, no disciplinary actions in the past 6 months, successful completion of prior training, and knowledge sharing within 6 months post-training.
- Retention-based forgiveness schedule applies after program completion.
So what are the next steps? Our team is eager to learn about you! Send us your resume or LinkedIn profile below and we'll explore working together!
Skills Required
- 7+ years of experience in data engineering or related disciplines such as data architecture or analytics engineering
- Demonstrated expertise in semantic layer and KPI or metric modeling
- Hands-on experience building and maintaining Databricks Metric Views or equivalent semantic or metric layer tooling
- Advanced proficiency in SQL and Python for data processing, transformation, and pipeline development
- Solid understanding of cloud data platforms, specifically Azure Databricks, and modern ELT or ETL tooling
- Expertise in data modeling techniques, conformed dimensions, and Medallion-style architectures
- Experience profiling data quality, lineage, and reconciliation across multiple source systems
- Experience working directly with business stakeholders to gather, validate, and implement KPI requirements
- Understanding of business ontology and semantic modeling concepts
- Proficiency with Git version control and collaborative development practices
- Knowledge of data engineering for AI or LLM-based analytics, including feature preparation for narrative generation and conversational analytics
- Advanced English proficiency
- Experience with FMCG, CPG, or retail data ecosystems, including POS, SKU, category, and market performance datasets
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.
