Lead Data Architect

🕒 July 28

🇺🇸 United States – Remote

💵 $190k / year

⏰ Full Time

🟠 Senior

🚰 Data Engineer

👻 Ghost score 5%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Data Meaning

WebsiteLinkedInAll Job Openings

51 - 200 employees

💼 Consulting

📦 Logistics

☁️ SaaS

Consulting • Logistics • SaaS

Data Meaning is a company that specializes in accelerating analytical workloads for businesses, helping them to increase productivity and focus on high-value tasks. They offer managed services, expert-on-demand solutions, and enablement & training programs to help organizations handle increased workloads and embrace new technologies effectively. With over 20 years of experience and more than 800 projects completed, Data Meaning provides expertise in data governance and strategy, aligning data initiatives with business objectives. They target bottlenecks in productivity and offer solutions in sectors like public sector and other industries to improve efficiency and compliance. Their services are designed to help businesses outsource mundane tasks, increase team engagement, and boost work efficiency through specialized skills and frameworks.

📋 Description

• Lead architecture assessment and documentation of the current data platform (Snowflake, Airflow, AWS) • Design the future-state data platform architecture following modern ELT and dbt best practices • Define standards for: data modeling, pipeline architecture, transformation frameworks, governance and data quality • Lead the migration of transformation logic into dbt models • Define architectural patterns for: staging layers, intermediate layers, data marts • Implement automated testing frameworks within dbt • Lead discovery sessions and technical workshops with the client’s data engineering teams • Document architecture, pipelines, and operational processes • Conduct architecture walkthrough sessions during knowledge transfer phases • Define standards for CI/CD, monitoring frameworks, and data quality validation • Establish schema management and governance best practices • Optimize Snowflake compute usage and platform performance • Design scalable orchestration strategies for Airflow pipelines • Improve platform reliability, monitoring, and operational observability

🎯 Requirements

• 10+ years of experience in Data Engineering or Data Architecture • Strong experience designing and implementing modern data platforms • Expert-level experience with Snowflake • Strong experience implementing dbt transformation frameworks • Advanced SQL modeling and performance optimization • Strong experience with Apache Airflow orchestration • Strong Python programming skills • Experience designing: ELT pipelines, Data warehouse architectures, Transformation frameworks, Large-scale data pipelines • Experience working in cloud environments (AWS preferred)