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Open nowPosted 11 days ago

2101 Data Engineer

inallmedia-llc73 open roles

Where
Brazil, Remote
Work mode
Remote
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Your applicationOpen now2101 Data Engineerinallmedia-llc · Brazil, Remote

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This job: posted 11 days ago

The posting

📌 Position: Senior Data Engineer

Location: Remote from LATAM

Contract Type: Full-time vendor

Time Zone Alignment: EST (±2 hours overlap)

🧭 About Inallmedia.com

Inallmedia.com is a global technology and design firm focused on building impactful digital solutions through remote, distributed teams across LATAM. We partner with international clients across industries, providing long-term technical expertise, product innovation, and team augmentation.

🚀 Project Overview

This role is embedded within a centralized Human Resources Data Mart (HRDM) engineering team, specifically supporting the Analytics product area. The primary objective is to maintain a secure, high-quality HR data warehouse that powers workforce analytics across international enterprise Centers of Excellence (COEs) and executive leadership.

As a Senior Data Engineer, you will build data pipelines and cloud infrastructure using dbt, AWS, Snowflake, and BigQuery, bridging traditional data architecture with modern AI capabilities. You will construct a dbt-based semantic layer and establish Model Context Protocol (MCP) and Snowflake Cortex workflows, enabling business leaders to query certified workforce data via natural-language AI agents.

🔍 Key Responsibilities

  • Data Pipeline Engineering & ETL/ELT: Develop, optimize, and maintain secure data pipelines using dbt, PySpark, Python, and Apache Airflow to ingest HR data sources from REST APIs, real-time streams (e.g., Google Sheets, web APIs), and external platforms.
  • Infrastructure & Cloud Resource Tooling: Provision, configure, and maintain scalable infrastructure for extraction, transformation, and loading processes utilizing Terraform, AWS Glue, AWS EMR, and AWS S3.
  • Data Warehouse & Data Mart Modeling: Design and maintain optimized data models, data marts, and warehouse structures across Snowflake and Google BigQuery.
  • Semantic Layer & Ontology Architecture: Build semantic views and ontology layers (mapping CORE → SEMANTIC → METRICS) on top of dbt models to provide business-friendly entity abstractions for BI tools and LLM agents.
  • AI Agent & MCP Deployment: Configure and deploy generative AI query agents using Snowflake Cortex (Cortex Analyst, Cortex Search) and maintain MCP (Model Context Protocol) connections between Snowflake and internal AI tools for secure, natural-language data querying.
  • Pipeline Monitoring, Quality & Compliance: Monitor data pipelines to enforce 99.5% uptime, build automated tests within a Data Quality Framework, ensure secure handling of sensitive data (PII), and produce comprehensive technical documentation.

💡 Must-Have Skills

  • Experience Level: 5+ years of dedicated, hands-on data engineering experience.
  • Data Warehousing & Transformation: Deep expertise in Snowflake (data modeling, datamarts, warehouse design), Google BigQuery (querying and optimization), and dbt (data transformations).
  • Programming & Ingestion: Strong object-oriented Python scripting capabilities and hands-on experience with REST API integrations and real-time data ingestion.
  • Orchestration & Querying: Proficiency in Apache Airflow for pipeline orchestration and advanced, highly optimized SQL writing skills.
  • Governance & Security: Demonstrated experience in the secure handling of large-scale, sensitive data (PII) and writing comprehensive technical documentation.
  • Experience working in Agile teams and remote, distributed environments.
  • Advanced/Fluent English communication skills for daily interaction.

🌟 Nice-to-Have Skills

  • Semantic Layer Design: Experience in semantic modeling design to provide business-object abstractions over dbt and warehouse models.
  • LLM-Native Query Layers: Hands-on experience with Snowflake Cortex (Cortex Analyst, Cortex Search) or equivalent LLM query frameworks.
  • Agentic Frameworks: Exposure to MCP (Model Context Protocol) or similar tool-calling and context-exposure frameworks.
  • Context Engineering: Familiarity with prompt and context engineering to ground AI agents in certified enterprise data sources.

🌐 Time Zone & Collaboration

The role requires collaboration with teams aligned to US Eastern Standard Time (EST). Flexibility to overlap a minimum of 4 core working hours with US time zones is expected.

💬 Language

All interviews, documentation, and daily communication will be in English.

#LI

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