- Proven experience as a Data Architect, Data Lead, or senior Data Engineer with architecture ownership responsibilities.
- Deep hands-on experience designing and governing production data pipelines built with dbt and Apache Airflow.
- Strong experience with Amazon Redshift data warehouse architecture and data mart design.
- Strong SQL and data modeling skills, with the ability to define and enforce modeling standards across a team, including data lineage, traceability, and auditability standards for a pipeline.
- Strong hands-on experience with Python for pipeline and tooling development.
- Experience with MySQL or similar relational databases, and familiarity with AWS cloud-native services such as S3, Lambda, EC2, and RDS.
- Comfort working within a regulated or compliance-driven environment (e.g., SOC-2, HIPAA, or similar), including justifying technical decisions for audit purposes.
- Experience providing technical leadership and mentorship to other data engineers.
- Experience working in a sprint-based agile delivery model, coordinating with cross-functional engineering teams.
- Strong documentation and communication skills; able to work effectively with both technical and non-technical stakeholders, including project-side architecture and product leadership.
- Advanced English.
- Comfortable using and evaluating AI-assisted development tools — AI coding assistants, code generation, or automated review tools — to improve team delivery speed and quality.

We are looking for someone able to

- Own technical architecture and design standards for the data pipelines supporting assigned projects, reviewing and approving technical designs for new pipeline components, Airflow DAGs, and data mart changes.
- Define and evolve dbt model standards — naming conventions, layering, and testing practices — ensuring consistency, quality,



and maintainability across the data team's model layer.
- Provide technical leadership and direction to the data engineering team: architectural guidance, design/code review, and mentoring for data engineers and analytics engineers on best practices.
- Partner with project stakeholders and the delivery team to align technical implementation with the project's architecture, roadmap, and sprint cadence, representing the data team in architecture discussions and technical planning.
- Own the technical strategy for addressing technical debt — investigating root causes of significant issues and proposing architecture-level remediation plans — and evolving pipeline architecture to support future features.
- Ensure the pipeline architecture supports end-to-end traceability, auditability, and compliance with SOC-2 controls and applicable regulated data handling requirements (e.g., HIPAA), documenting architecture decisions, standards, and data lineage.
- Support knowledge transfer and onboarding as project team composition changes, guiding the team's approach so documentation and standards remain transferable.
- Track architecture and technical debt work using the project's tooling (e.g., JIRA), and ensure code changes follow branching and CI/CD standards (e.g., GitLab).
- Support incident response and root-cause analysis for architecture-level or systemic pipeline issues.

Other valuable skills

- Experience with drug supply chain, pharmacy licensing, or similar healthcare regulatory frameworks.
- Experience with state-level or federal regulatory and compliance systems.
- Prior work with not-for-profit or governmental organizations.
- Experience with GitLab CI/CD, AWS CloudWatch, AWS Secrets Manager, or infrastructure-as-code practices.
- Familiarity with C#.
- Experience presenting architecture decisions to technical or product leadership.

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📌 Data Architect (Buenos Aires)
🏢 Light-It
📍 Buenos Aires