Data Architect

Join DaCodes!
We are a firm of experts in high-impact software and digital transformation.
For 10 years, we have created solutions focused on technology and innovation thanks to our team of nearly 300 talented #DaCoders, including developers, architects, UX/UI designers, PMs, QA testers, and more. Our team collaborates on projects with clients in LATAM and the United States, delivering outstanding results.
At DaCodes, you will have the opportunity to advance your professional development, work on diverse projects across different industries, and contribute to the design, implementation, and optimization of cloud infrastructures.
Our DaCoders have a major impact on the success of our business and our clients. You will be the expert participating in our projects and will have access to disruptive startups and global brands.
Interested?

We are looking for a Senior Data Architect to lead the modernization of legacy ETL workloads into scalable cloud lakehouse architectures on Azure.

This role combines architecture ownership, hands-on technical analysis, and guidance for up to three engineers or developers working simultaneously. You will investigate existing production workloads, define migration patterns, and make design decisions when requirements or documentation are incomplete.

You will also provide technical oversight within an AI-assisted modernization workflow, validating automated analysis, approving implementation plans, and resolving exceptions that require human judgment. Clear, auditable documentation and accountability for technical decisions are central to the role.

Location: Remote, open to candidates based in Mexico and Latin America.
Language: English required for technical discussions, collaboration, and documentation.

Requirements

Main responsibilities

• Analyze legacy production workloads: Reverse engineer Alteryx workflows, SSIS packages, and SQL Server jobs to identify actual behavior, undocumented dependencies, historical fixes, and implicit business logic.
• Validate automated analysis: Audit reports generated by analysis agents against original source artifacts, resolving discrepancies, omissions, and unsupported assumptions.
• Own cloud architecture decisions: Define the target lakehouse architecture and establish ingestion patterns, data types, partitioning strategies, naming conventions, and scalability standards.
• Define governance and security: Establish data modeling, lineage, access, and governance practices, including security policies in Unity Catalog.