Analytics Engineering Lead

Blend360•Columbia, MD

About The Position

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. 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.

Requirements

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

Nice To Haves

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
Exposure to BI layers (Looker, Tableau) built on top of dbt-modeled data

Responsibilities

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
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
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
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