Analytics Engineer

• Develop, maintain, and optimize data transformation pipelines using DBT
• Build and manage complex data workflows using Apache Airflow
• Design and implement dimensional and relational models to support strategic analytics and KPIs
• Ensure data quality, reliability, and traceability through testing, monitoring, and engineering best practices
• Model, organize, and make datasets available for analytical consumption
• Develop and optimize high-performance SQL queries across large volumes of data
• Work with BigQuery to drive operational efficiency, cost optimization, and performance
• Develop and maintain dashboards and semantic layers using the Looker platform
• Promote best practices in data documentation, governance, and cataloging
• Support business teams in defining metrics, KPIs, and analytical requirements
• Participate in architecture discussions and contribute to the evolution of the organization’s data platform
• Automate processes and routines using Python whenever necessary
• Serve as a technical reference for the team, sharing knowledge and best practices

Senior Analytics Engineer – DBT

• Work for a financial institution, building the data transformation and consumption layer in a Google Cloud environment
• Take data from source to dashboard, ensuring quality, performance, and cost control
• Build, test, and document data transformation models using DBT
• Develop and optimize SQL queries and dimensional and relational models in BigQuery
• Monitor and optimize BigQuery processing performance and costs
• Build, maintain, and orchestrate data pipelines in Airflow
• Develop dashboards and semantic models in Looker Platform
• Gather requirements from business stakeholders and translate them into data solutions
• Conduct root-cause analyses and propose insight-driven solutions
• Document and tag data assets, promoting data governance best practices
• Automate tasks and perform exploratory analyses with Python

Senior Analytics Engineer, DBT, GCP

• Serve as the Recruitment and Selection focal point for the Data Analytics Tower, supporting multiple clients and projects
• Align on role profiles with managers, operations, and other stakeholders, understanding technical, behavioral, and business requirements
• Lead high-volume, highly complex recruitment processes, particularly for Data and Analytics positions
• Manage hard-to-fill roles by assessing market conditions, talent availability, salary ranges, and hiring challenges
• Develop sourcing and attraction strategies for specialized professionals and scarce talent profiles
• Track metrics, deadlines, and position progress, identifying bottlenecks and proposing action plans
• Act as a consultant to management, providing market insights, identifying risks, and presenting hiring alternatives
• Support vacancy prioritization according to operational needs and project criticality
• Lead recruitment processes for positions requiring English, including roles in multicultural and/or international environments
• Maintain close communication with managers and operations throughout the recruitment process
• Support the development and continuous improvement of recruitment strategies for the Tower
• Serve as the primary link between Recruitment and Selection, management, and Data Analytics Tower operations

Recruitment Specialist – Data Analytics

• Understand the needs of business teams and translate them into data transformation and modeling rules.
• Develop and maintain SQL and Python scripts to process, combine, and structure data available in the initial layers of the Data Lake.
• Build and enhance consumption tables and databases in Databricks, making them easier for the Analytics team and business areas to use.
• Apply and validate business rules, ensuring the consistency of the information made available.
• Investigate data discrepancies, identify their causes, and make the necessary adjustments in partnership with the teams involved.
• Participate in the alignment of demands and priorities with business teams, communicating progress, questions, and blockers.
• Follow the team’s development standards, using code version control and the CI/CD pipeline in GitHub.
• Collaborate with senior professionals to solve problems and propose improvements to data transformation and delivery processes.

Mid-Level Data Engineer, Analytics

• Build reliable analytical models and datasets
• Structure official metrics and indicators
• Work on the Refined/Analytics layer
• Ensure consistency across data, metrics, and dashboards
• Support Power BI and other data consumers
• Create reusable models for products and the platform
• Work closely with Data Scientists and Data Engineers
• Support the definition of data quality and reconciliation rules
• Contribute to the data catalog, documentation, and lineage
• Reduce reliance on consumer-specific solutions
• Connect data engineering with the platform’s analytical consumption layer
• Transform raw data into reliable, reusable information aligned with the needs of products, the platform, and leadership
• Collaborate with Engineering, Data Science, Product, and Analytics teams to transform data into actionable information

Mid-Level Analytics Engineer

• Ensure data quality across the team, supporting the growth of the active customer base
• Ensure business decisions and internal models are grounded in accurate data
• Build an understanding of Brazilian Open Finance data through organization, testing, and advanced data processing
• Enhance Open Finance data pipelines, transforming raw information into features for LINA’s products
• Work on credit risk modeling, transaction categorization, and language model projects
• Continuously improve data quality and internal processes
• Develop and maintain financial transaction categorization pipelines
• Build features derived from Open Finance data for products and internal models
• Prepare features and pipelines for modeling initiatives
• Collaborate with the data squad to prioritize and resolve requests
• Document pipeline transformations, business rules, and technical decisions
• Monitor the health and performance of production pipelines and propose continuous improvements
• Generate insights, models, and metrics for clients

• Build and maintain Azure Data Factory pipelines, ingestion flows, and Bronze layer data contracts under the Data Engineer Lead's direction
• Manage source system integration points: Infor SyteLine, Infor ION, Dynamics CRM, Workday, OneStream, and Fusion PLM
• Ensure pipeline reliability, SLA compliance, and incident response for all ingestion flows
• Apply platform standards and CI/CD practices established by the Core Team's Data Architect
• Support Bronze schema definition and validation
• Support ingestion pipeline architecture design in partnership with the Data Engineer Lead
• Escalate and troubleshoot incidents affecting pipeline reliability

Senior Data & Analytics Specialist

• Own the company-wide data roadmap and prioritize platform investments
• Build automated data quality and freshness monitoring across source data, pipelines, and reports
• Manage data lineage and impact analysis from production sources through warehouse models to dashboards
• Administer Lightdash, including workspace structure, permissions, certification, naming, rollout, enablement, query performance, and warehouse costs
• Extend and govern the semantic layer with one official definition per metric
• Govern Segment event tracking, including event standards, ownership, property dictionaries, review lifecycle, and drift detection
• Govern data access for AI tools and maintain an AI-legible warehouse
• Manage data security, PII handling, retention, and periodic access reviews under US state privacy laws
• Establish documentation, ownership models, and review loops for sustainable governance
• Lead incidents to resolution and connect production change review with downstream impact assessment
• Start as a solo contributor and later hire and manage a Lead Analytics Engineer

Analytics Engineering Manager, Data Platform – Governance

• Perform business and data analysis to support internal initiatives and external/client-driven requests
• Investigate and analyze data issues and bugs, identify root causes, and work with appropriate teams to drive resolution
• Proactively identify data quality issues, inconsistencies, and potential problems across data models and services
• Ensure the quality, accuracy, and reliability of data services and deliverables through continuous validation and monitoring
• Review and maintain clear, accurate, and up-to-date documentation for data models, business rules, metrics, processes, and services
• Validate business rules, KPIs, and data transformations against business requirements and expected behavior
• Contribute to data governance and standardization, including consistent definitions, structures, naming conventions, and data quality standards
• Collaborate with the Data Modeling & Governance Team and cross-functional teams across the organization

• Define and evolve the semantic layer and business metrics, aligned with dimensional modeling (Kimball) and the Medallion architecture
• Develop and maintain dbt models in the analytics layer (Gold marts), including incremental models, automated tests, documentation, and reusable macros
• Build and govern analytical products in Power BI, including semantic models, DAX, RLS, publication to the Power BI Service, certified datasets, and organized workspaces
• Evolve explores and views in Looker (LookML), measures, derived tables, multi-tenant governance, and embedding where applicable in SaaS products
• Write advanced SQL on Azure Synapse Analytics, BigQuery, and consumption views, with a focus on performance for BI and self-service analytics
• Implement downstream quality practices, Data Contracts, testing, and monitoring
• Serve as a technical reference at the intersection of data engineering, business, and product
• Promote DataOps, Git/PRs, code reviews, and metrics documentation
• Support AI/ML initiatives with clean, stable, and well-defined analytical data foundations

Analytics Engineer jobs in Brazil

Senior Analytics Engineer – DBT

Senior Analytics Engineer, DBT, GCP

Recruitment Specialist – Data Analytics

Mid-Level Data Engineer, Analytics

Mid-Level Analytics Engineer

Mid-Level Analytics Engineer

Senior Data & Analytics Specialist

Analytics Engineering Manager, Data Platform – Governance

Analytics Engineer

Data Analyst – Analytics Engineer