Analytics Engineering Lead

Company Description

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 Description

Design and own the curated data model layer that transforms raw ingested data into business-ready entities consumed by a Customer Data Platform (CDP) and business users. This role sits at the core of a large-scale data initiative  you'll be the person who takes 18 distinct data domains and makes them trustworthy, accessible, and activation-ready.

THE ROLE

We're looking for an Analytics Engineering Lead to architect and deliver the curated layer of a modern data platform. You'll work at the intersection of data architecture, engineering rigor, and business strategy  translating complex, multi-domain data challenges into scalable, well-modeled solutions that teams actually trust and use.

This is a hands-on technical leadership role. You'll report directly to the Internal Technical Architect and serve as the subject matter expert on dbt, dimensional modeling, and data quality. You'll also be a critical bridge between engineering and business stakeholders  ensuring model outputs match real activation needs.

Curated Data Modeling

• Design curated models across all 18 data domains — including Identity, Engagement, Demographics, Propensity, Geographic, Behavioral, Products, and Segmentation — transforming raw ingested data into clean, business-ready entities
• Implement a structured dbt project architecture following the staging → intermediate → marts pattern, with full testing and documentation at every layer
• Build an enrichment layer that combines first-party data with third-party signals to produce high-value, activation-ready outputs

Data Quality & Observability

• Define and enforce a comprehensive data quality framework covering freshness checks, volume anomaly detection, and schema drift alerting
• Establish testing standards and documentation practices that make the data layer auditable and trustworthy for both technical and business consumers

Stakeholder Engagement

• Interface directly with business stakeholders to validate that model outputs align with CDP activation needs and downstream consumption requirements
• Translate business requirements into data model specifications, and translate model constraints back into business-friendly language

Standards & Leadership

• Set analytics engineering best practices across the project — naming conventions, modular design patterns, incremental model strategies, and code review standards
• Partner with data engineers, CDP architects, and analysts to ensure seamless data flow from ingestion through activation

Qualifications

• 8+ years of experience in data engineering, analytics engineering, or a closely related discipline
• 3+ years in a lead or senior role with direct influence over technical standards and project direction
• Expert-level dbt proficiency  this is the core tool of this role; you should be deeply fluent in macros, packages, incremental models, advanced testing strategies, and project structuring
• Expert-level SQL across complex transformations, window functions, and performance optimization
• Strong dimensional modeling skills  you understand how to design for both analytical flexibility and CDP consumption
• Hands-on experience building data quality frameworks (freshness, anomaly detection, schema drift)
• AWS data stack experience specifically Athena and/or Redshift
• Demonstrated ability to work directly with business stakeholders and validate model outputs against activation requirements
• Marketing data domain expertise  including identity resolution, engagement modeling, propensity scoring, and segmentation

Preferred

• Experience with orchestration tools such as Airflow, Prefect, or Dagster
• Familiarity with CDP platforms and how curated data layers feed activation workflows
• Background in data observability tooling (Monte Carlo, Elementary, or similar)
• Experience working in a consulting or client-services environment
• Expos