Data Architect

2 semanas atrás

Porto Alegre, Rio Grande do Sul, Brasil Inbenta Tempo inteiro

About Inbenta

Inbenta is a global technology company that develops an Automatic Language Processing (NLP) engine, providing answers to human intentions formulated in natural language. Our company builds conversational solutions for businesses, including chatbots, knowledge management tools, digital demos, and search engines. We serve global blue-chip companies in various sectors, such as Banking, Insurance, Automotive, Services, Retail, Transportation, and more.

Job Overview

The Data Engineer plays a crucial role in harnessing the power of a unified data and analytics platform built on AWS and Snowflake to deliver consistent, high-value business insights. This role involves implementing domain-driven designs for data models, APIs, and products, as well as collecting, translating, and validating data for analysis.

Key Responsibilities

  • Develops enterprise solutions related to operational data stores, data warehouses, analytics, and business intelligence.
  • Designs and develops Data Models, Data Pipelines, Data Warehouses, Data Lakes, ETL/ELT processes.
  • Executes modern data engineering principles and data modernization practices.
  • Provides expertise across the Data & Analytics domain, including data storage (Data Lake & Data Warehouse), data integration, business intelligence, artificial intelligence & machine learning.
  • Prepares technical design specifications and other documentation.
  • Ensures data is secure and compliant under regulations.
  • Continuously improves efficiency, accuracy, and effectiveness of the data.

Requirements

Education, Experience & Minimum Requirements

  • Bachelor's degree in computer science, data science, or a relevant field.
  • Required fluency in English (both written and spoken).
  • A minimum of 6 years of designing, building, and managing modern enterprise data.
  • SAS-based Tech, Banking, insurance, or other similar financial services experience is strongly preferred.

Technical Competencies

  • AWS, Google, or Azure (SQL, Data Factory, Data Hub, etc.).
  • Containerization (i.e., Kubernetes).
  • Graphite, Splunk, Kafka, Snowflake.
  • API management/gateway, JSON, Python, Query tuning.
  • Excellent knowledge of best practices, industry trends, and compliance requirements in Data Modeling, Governance, and Data Management.
  • Strong knowledge of data profiling, Data standardization, transformation, Data cleansing.
  • Strong experience building big data solutions using Data Lake, Data Warehouse, Data Products.
  • Expert-level understanding of different data storage technologies and appropriate use cases for each – Relational, NoSQL, Time-Series, Graph, etc.
  • Expert-level understanding of data-integration technologies – ETL/ELT, Streaming, Replication, etc.

Functional Competencies

  • Excellent interpersonal, written, and verbal skills.
  • Strong strategic mindset in connecting business drivers and a foundational understanding of company data in support of data-driven approaches to analytics and insights.
  • Strong attention to detail.

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