Remote Bi Engineer — Build Ai-Ready Data Pipelines

Sunnydata; Ciudad de Mendoza; Hybrid; ARS 136,310,000 - 181,747,000; Full time

Benefits offered by this job

Professional growth

Competitive compensation: USD-basedPay

Exciting projects

Flextime: Remote and office options

Job summary

Sunnydata is seeking a Senior/Lead Data Engineer to build and maintain pipelines and data models behind a centralized HR data warehouse. You will work with Snowflake, DBT, Python, and Airflow to enable BI tools and AI agents to access certified HR data.

Experience with Snowflake Cortex or MCP-based tooling for agentic querying is a strong plus. Demonstrated ability to design scalable data architectures, maintain data quality, and ensure security and privacy compliance.

Qualifications

• 4+ years of data engineering experience.
• Experience with Snowflake including data modeling.
• Proficiency in DBT for data transformations.
• Strong Python programming and data scripting.
• Airflow for pipeline orchestration.
• Experience with BigQuery querying and optimization.
• Secure handling of large-scale data.
• REST API integration and data ingestion.

Responsibilities

• Develop and maintain secure, efficient data pipelines using DBT, PySpark, and Python.
• Build infrastructure for ETL from diverse sources with Python, DBT, Terraform, and AWS.
• Improve processes, run data quality tests, and monitor pipelines for high uptime.
• Design semantic views and ontology layers on dbt models for BI and AI.
• Develop and deploy AI agents for HR data querying via Snowflake Cortex.
• Configure MCP connections between Snowflake and internal AI tools.

Skills

Data engineering experience

Strong SQL

REST API integration

Real-time data processing

Data modeling

Data warehousing

Tools

Snowflake

DBT

Python

Airflow

Google BigQuery

Terraform

AWS

Job description

WHY JOIN US

If you’re looking for a place to grow, make an impact, and work with people who care, we’d love to meet you!

ABOUT THE ROLE

We are looking for a Senior/Lead Data Engineer to build and maintain the pipelines and data models behind a centralized HR data warehouse. Working in Snowflake, DBT, Python, and Airflow, this person designs semantic layers that make HR data consumable by both BI tools and AI agents. Experience with Snowflake Cortex or MCP-based tooling for agentic querying is a strong plus.

WHAT YOU WILL DO

• Develop and maintain secure, efficient data pipelines using DBT, PySpark, and Python applications within a supported cloud environment for HR data sources.
• Build the infrastructure and tooling required for optimal extraction, transformation, and loading of data from a wide variety of sources using Python, DBT, Terraform, and AWS technologies such as Glue, EMR, and S3.
• Identify and implement internal process improvements, develop tests within the Data Quality Framework, monitor pipelines to maintain 99.5% uptime, and create and maintain the data models used by partner teams.
• Design and build semantic views and ontology layers on top of existing dbt models (CORE > SEMANTIC > METRICS) so HR data is consumable by both BI tools and AI agents/LLMs, defining business‑friendly entities, relationships, and metrics that abstract away raw warehouse structure.
• Develop and deploy AI agents (e.g., via Snowflake Cortex Analyst/Cortex Search) for HR COE use cases — including PE, iCIMS, LMS, and Sales/CS Worker agents — enabling natural‑language querying over certified HRDM data.
• Configure and maintain MCP (Model Context Protocol) connections between Snowflake and internal AI tooling to securely expose HR data to conversational and agentic interfaces.

MUST HAVES

• 4+ years of experience.
• Snowflake, including data modeling, datamarts, and data warehouse design.
• DBT for data transformations.
• Python, including object‑oriented programming and data scripting.
• Airflow for pipeline orchestration.
• REST API integration and data ingestion.
• Google BigQuery querying and optimization.
• Secure handling of large‑scale and sensitive data.
• Real‑time data processing (e.g., from Google Sheets or APIs).
• Strong SQL, including highly optimized queries.

NICE TO HAVES

• Semantic layer / semantic modeling design, providing business‑object abstraction over dbt and warehouse models.
• Snowflake Cortex (Cortex Analyst, Cortex Search) or equivalent LLM‑native query layers.
• MCP (Model Context Protocol) or similar tool‑calling / context‑exposure frameworks.
• Familiarity with prompt and context engineering for grounding LLM agents in certified data sources.

PERKS AND BENEFITS

• Professional growth: Mentorship, TechTalks, and personalized growth roadmaps.
• Competitive compensation: USD‑based pay with education, fitness, and team activity budgets.
• Exciting projects: Modern solutions with Fortune 500 and top product companies.
• Flextime: Flexible schedule with remote and office options.