Senior Data Engineer (AI)

Location: Hybrid role in Guadalajara, Mexico

Company

hirify.global develops cybersecurity solutions that help organizations create, secure, and operate applications and digital services.

What you will do

• Design, develop, and ship enterprise data products, dbt transformation logic, and Python-based data workflows.
• Build and optimize Snowflake and dbt assets, including models, stored procedures, tests, macros, and access patterns.
• Design dimensional, logical, and semantic data models with reusable business rules, metrics, validation logic, and data definitions.
• Integrate data from enterprise systems, product telemetry, files, APIs, SaaS platforms, and cloud services.
• Build Python-based and Streamlit data applications for analytics, operational workflows, and business-facing interactions.
• Lead complex initiatives through deployment and adoption, mentor engineers and contractors, and establish engineering standards and automation.

Requirements

• 8+ years of experience in data engineering, data platform engineering, analytics engineering, or a related technical role.
• Advanced SQL skills, including query optimization, complex transformation logic, and data modeling.
• 5+ years designing data models, data marts, semantic layers, data warehouses, or enterprise data standards.
• 5+ years developing ETL/ELT, transformation logic, curated data products, or application-ready data layers with Snowflake, dbt, Azure Data Factory, Fivetran, or equivalent tools.
• 3+ years of Python development for data engineering, automation, API and SaaS integration, custom applications, or data science pipeline support.
• Experience with productionizing enterprise data products, source control, CI/CD, and performance and cost optimization.

Nice to have

• Deep Snowflake experience, including performance tuning, Snowpipe, Snowpark, secure views, data sharing, role-based access controls, and cost optimization.
• Advanced dbt experience with macros, tests, documentation, exposures, governance, semantic layers, and CI/CD, plus Streamlit or similar frameworks.
• Experience with natural language interfaces, agent-assisted workflows, LLM data consumption, prompt grounding, token usage, inference cost, and response latency.
• Experience with Databricks, Spark, Delta Lake, or MLflow and support for machine learning workflows.
• Experience across enterprise domains such as customer success, support, sales, finance, subscriptions, installed base, product telemetry, or software fulfillment.

Culture & Benefits

• Work in a diverse environment centered on improving experiences for customers and their customers.
• Contribute to governed, secure, hardened, and production-ready data capabilities.
• Partner with analytics, data science, product, and business teams on reusable data products and AI-enabled experiences.