Data/ Solution Architect

Summary of role

As a Data/Solution Architect, you will design and build the intelligence backbone of MoonTech. You will architect a modern, scalable data platform that powers advanced analytics, AI use cases, marketing intelligence, and product decision-making.

This is a hands-on technical leadership role — ideal for someone who enjoys building systems from the ground up, writing production-grade code, defining architectural standards, and shaping the future of a data function. You will serve as the foundational data hire, setting the standards, infrastructure, and vision that will scale with the company.

Requirements & Qualifications

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related technical field.

• 8–10 years of experience in Data Engineering or Data Architecture.

• Strong hands-on coding expertise in SQL and Python.

• Experience building modern cloud data platforms (Google Cloud preferred).

• Advanced experience with DBT and data modeling best practices.

• Experience designing and implementing batch and streaming data pipelines.

• Strong knowledge of data orchestration tools and workflow management.

• Deep understanding of performance optimization, scalability, and system reliability.

• Experience supporting analytics and AI use cases (forecasting, attribution, ML workflows).

• Familiarity with data governance, data quality frameworks, and scalable architecture standards.

• Experience in marketing, media, or performance-driven data environments is highly preferred.

• Strong communication skills with the ability to translate complex architecture into business language.

Responsibilities

• Design and implement a scalable, modern cloud-based data platform.

• Architect and maintain batch and streaming data pipelines.

• Define and enforce data modeling standards and governance practices.

• Ensure platform performance, scalability, reliability, and security.

• Lead technical design decisions and define long-term architectural roadmaps.

• Review code, enforce engineering best practices, and guide future data hires.

• Design data foundations to support forecasting, attribution modeling, CDP integrations, and marketing analytics.

• Collaborate with Product, BI, and Data Science teams to create analytics-ready datasets.

• Act as the internal technical authority for all data architecture decisions.

Key Skills:

Data Architecture • Cloud Data Platforms • DBT • Advanced SQL • Python • Streaming Pipelines • Data Modeling • AI Enablement • Performance Optimization • Scalability • Technical Leadership • Stakeholder Communication • Marketing Analytics