About this role
Designing and implementing modern semantic models using tools like dbt forms the core daily routine, bridging complex operational databases with AI queries and analytical consumption. CodeRoad delivers end-to-end software development services and nearshore technology solutions to help businesses scale their infrastructure. Operating within this ecosystem, the Senior Data Engineer architects an agent-ready data foundation and semantic layer while optimizing low-latency query performance for downstream LLM retrieval. This position exists to anchor robust access control and data privacy policies across modern database environments, ensuring AI agents access accurate and secure data in real time.
Success in this function requires a strong ownership mindset, proactive problem-solving abilities, and smooth collaboration across nearshore team environments. Professionals who excel here enjoy building resilient ETL pipelines, tuning complex queries, and aligning data structures with the operational requirements of RAG architectures and Agentic AI workflows. Advanced English fluency in both written and spoken formats is strictly required for daily coordination with cross-functional AI engineering teams.
What you will do
- Design and implement modern semantic models including facts, dimensions, and metrics stores with tools like dbt.
- Build curated, high-performance analytical views and production ELT and ETL pipelines to streamline data retrieval and freshness.
- Optimize database query performance, indexing, and schema definitions for low-latency analytical workloads through query log analysis.
- Implement granular data governance frameworks such as Role-Based Access Control, Row-Level Security, and dynamic data masking for PII protection.
- Establish comprehensive data quality, pipeline monitoring, and anonymization standards across all analytical layers.
- Collaborate with cross-functional AI engineering teams to align data structures with RAG architectures and Agentic AI workflows.
What you need
- Bring four or more years of dedicated data engineering experience with strong, production-grade command of SQL across Azure SQL, PostgreSQL, or modern cloud data warehouses.
- Apply a solid foundation in dimensional modeling using Kimball methodology and semantic layer or metrics store architecture.
- Demonstrate proven production experience building and maintaining resilient ETL and ELT data pipelines.
- Showcase past work implementing database security policies, granular role-based permissions, and dynamic data masking or PII protections.
- Possess hands-on expertise tuning complex queries for low-latency analytical workloads.
- Contribute practical experience working with RAG applications and agentic AI frameworks.
- Communicate fluently in advanced written and spoken English.
Nice to have
- Experience with orchestration platforms such as Apache Airflow, Prefect, or Dagster
- Exposure to vector databases like pgvector, Pinecone, or Qdrant used in generative AI stacks
- Familiarity with streaming or event-driven technologies including Apache Kafka or AWS Kinesis
- Hands-on experience with cloud infrastructure as code using Terraform or cloud-native CI and CD automation
Benefits
- 100% remote culture allowing work from anywhere in Latin America
- Holidays off aligned with national and company calendars
- Paid Time Off to ensure work-life balance
- Health insurance assistance coverage
- Competitive USD compensation paid directly to you
- Growth opportunities within high-impact, modern tech projects and multi-client ecosystems
Skills and tech stack
Questions candidates ask
What is the remote work policy for this role?
The position features a 100% remote culture that allows you to work from anywhere in Latin America. You will collaborate within nearshore team environments.
How is compensation structured?
The role offers competitive USD compensation paid directly to you. Specific salary figures are not publicly disclosed in the posting.
What technologies and frameworks will I use?
You will work with SQL, Azure SQL, PostgreSQL, modern cloud data warehouses, and semantic layer tools like dbt. Additional stack elements include RAG applications, agentic AI frameworks, and optional exposure to tools like Airflow, vector databases, and Kafka.
What language proficiency is required?
Advanced English fluency in both written and spoken formats is strictly required for this position. This ensures smooth communication across cross-functional nearshore teams.