Senior Analytics Engineer

🕒 June 11

🇧🇷 Brazil – Remote

⏰ Full Time

🟠 Senior

📊 Analytics Engineer

👻 Ghost score 62%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

Dadoteca

WebsiteLinkedInAll Job Openings

51 - 200 employees

💼 Consulting

🏥 Healthcare

📣 Marketing

Consulting • Healthcare • Marketing

Dadoteca is a Brazilian data consultancy that transforms data into actionable knowledge through Business Intelligence, Advanced Analytics, Artificial Intelligence, automation, and data architecture & governance. As a Microsoft Gold Partner for Data Analytics and a Databricks Consulting Partner, Dadoteca delivers consulting and outsourcing services using Power BI, Azure, SQL Server and Python for clients across sectors (energy, retail/shopping centers, healthcare) in Brazil and internationally. The company focuses on tailored implementations, dashboards, ETL pipelines, automation and AI-driven solutions to improve strategic decision-making and operational performance.

📋 Description

• Work on building and evolving the organization’s analytical layer, transforming raw data into reliable, documented, and reusable data products. • Be responsible for structuring analytical models, Data Marts, and corporate metrics that support Business Intelligence, Analytics, and Machine Learning initiatives. • Ensure data quality, governance, scalability, and alignment with business needs. • Develop transformations and analytical models using DBT. • Build, maintain, and evolve analytical data layers, including Silver, Curated, and Gold. • Design and implement Data Marts oriented to business area needs. • Translate business rules and processes into scalable, standardized, and reusable data models. • Define, document, and govern corporate metrics, promoting consistency across teams. • Ensure data quality, traceability, observability, and reliability. • Develop automated tests and quality controls to validate data. • Support the provisioning of datasets for consumption by BI, Analytics, and Machine Learning teams. • Collaborate with Data Engineers, Data Scientists, BI Analysts, and business stakeholders. • Promote best practices in analytical modeling, documentation, versioning, and data governance. • Contribute to the evolution of analytical architecture and the organization of data layers. • Participate in defining standards and strategies for consuming corporate data. • Identify opportunities for optimization, performance improvements, and scalability of developed solutions.

🎯 Requirements

• Bachelor’s degree in Computer Science, Computer Engineering, Information Systems, Data Science, Statistics, Mathematics, Engineering, or related fields. • Advanced experience with DBT. • Advanced SQL skills. • Dimensional and analytical data modeling. • Experience with Data Warehouse and Lakehouse architectures. • Solid knowledge of analytical layers (Silver, Curated, Gold, and Data Marts). • Best practices in data quality, testing, and data governance. • Code versioning using Git and GitHub. • Technical and functional documentation of data models. • Definition, standardization, and governance of corporate metrics. • Structuring datasets for BI, Analytics, and Machine Learning. • Excellent communication with technical teams and business stakeholders. • Strong analytical and problem-solving skills. • Organized with attention to detail. • Strategic data and analytics product mindset. • Ability to translate business requirements into data solutions. • Proactive in identifying and implementing improvements. • Comfortable working in multidisciplinary teams. • Sense of responsibility for data quality and reliability. • Product-oriented mindset for data products. • Ability to work autonomously and provide technical leadership.

🏖️ Benefits

• Remote • Full-time • Outsourcing