Analytics Engineering Architect/Tech Lead

Role Overview 

The Analytics Engineering Tech Lead will be responsible for helping drive the strategy, design, and delivery of scalable data products that enable business stakeholders to make data-informed decisions. This role sits at the intersection of data engineering and analytics, ensuring that robust, governed, and high-quality data is available across the organization. They will act as a technical leader and collaborator, working closely with engineering, product, and business teams to shape the enterprise data landscape.  

Key Responsibilities 

Collaborates with senior business and technology stakeholders to define MVP scopes and design data solutions aligned with enterprise architecture strategy. 

Alongside with the lead team defines the architectural strategy for data analytics, visualization and activation, aligned with the company guideline. 

Guides the development and maintenance of ELT/ETL pipelines using modern tools and frameworks (e.g., dbt, Airflow), ensuring scalability, reliability, and data quality. 

Designs and maintains enterprise and solution-level data models, supporting both operational and analytical needs. 

Provides technical leadership to analytics engineers and data engineers, promoting best practices, code standards, and agile workflows. 

Facilitates collaboration across multidisciplinary teams, translating complex business requirements into sustainable data products. 

Required Qualifications & Skills 

Experience in data/analytics engineering, including leadership or tech lead responsibilities. 

Understanding of cloud architecture, especially Google Cloud Platform (GCP), including BigQuery, Dataflow, Pub/Sub, and Cloud Composer. 

Proficiency in SQL, dbt, and Python; experience with modern ELT/ETL development and data transformation best practices. 

Solid understanding of data modeling techniques  

Experience with BI tools (e.g., Tableau, Looker, Power BI)  

Experience leading technical teams and mentoring junior developers. 

Excellent communication and stakeholder management skills; ability to explain technical concepts to non-technical audiences. 

+ Plus: Knowledge in Machine Learning and experience with AI deployment.