Brasilseg

Data Engineer

• Define and evolve scalable, resilient, and efficient data architectures for analytics platforms and strategic projects.
• Establish technical standards, best practices, and data engineering guidelines across the information lifecycle.
• Design and implement data pipelines across the Bronze, Silver, and Gold layers, ensuring data quality, governance, and traceability.
• Develop and optimize data ingestion, transformation, and delivery processes in Databricks and AWS environments.
• Create and maintain connectors and integrations with various data sources, including APIs, relational databases, NoSQL systems, and enterprise applications.
• Develop reusable solutions, frameworks, and automations to increase productivity and standardize the data ecosystem.
• Implement data quality controls and frameworks, ensuring the consistency, integrity, completeness, and compliance of information.
• Monitor and optimize the performance of pipelines, queries, and distributed processing environments.
• Identify and resolve failures, bottlenecks, and structural issues in production environments.
• Establish and evolve processes for cataloging, governance, lineage, and access control for data assets.
• Evaluate new technologies, tools, and architectural approaches to continuously evolve the data platform.
• Serve as a technical reference for multidisciplinary teams, sharing knowledge and advancing data engineering practices.

Data Engineering Specialist

Data Analyst

• Build and enhance analytical data models and Data Marts to support Business Intelligence, Analytics, and Artificial Intelligence initiatives.
• Develop and optimize data structures across analytical layers, ensuring performance, scalability, and reliability.
• Conduct exploratory and diagnostic analyses using SQL and Python to identify opportunities and trends and generate business insights.
• Develop and maintain management dashboards and reports in Power BI.
• Ensure data quality through validation, testing, and KPI monitoring.
• Map, document, and catalog business rules, KPIs, and data assets.
• Partner with business and technology teams, translating strategic needs into analytical solutions.
• Support the advancement of a data-driven culture by promoting the conscious and efficient use of information.
• Contribute to data governance initiatives, ensuring traceability, standardization, and accessibility of corporate data.
• Influence decision-making through data-driven analyses, metrics, and recommendations.

Senior Data Analyst

• Work as a Mid-Level Data Analyst in the Data Governance and Engineering department.
• Develop and maintain analytical models, Data Marts, and aggregated tables across the Bronze, Silver, and Gold layers of the Databricks ecosystem.
• Translate business rules into queries and data models that support decision-making.
• Conduct exploratory and diagnostic analyses to identify trends, opportunities, and operational deviations.
• Develop and optimize data transformation and manipulation routines using SQL, Python, and PySpark.
• Independently perform data extraction, cross-referencing, and reconciliation activities.
• Ensure the quality, consistency, integrity, and accuracy of data made available to users.
• Identify and support the resolution of inconsistencies between source systems and analytical databases.
• Document business rules, metrics, data dictionaries, and assets in the corporate catalog.
• Support the governance and standardization of corporate metrics.
• Address analytical requests from business areas and participate in requirements gathering for new data solutions.

Data Analyst, Mid-Level

Actuary

• Support data analysis using statistical techniques to help understand business risks.
• Assist in identifying internal and external factors that may impact the company, such as market changes, new regulations, or economic conditions.
• Conduct data analyses to identify patterns and generate insights under the supervision of the team.
• Contribute to data organization and processing, supporting the development of models and trend analyses.
• Assist in preparing reports and dashboards to facilitate data visualization and monitoring.
• Support the calculation and monitoring of risk indicators.
• Collaborate in preparing periodic Solvency and Liquidity reports for internal and external clients.
• Support the transformation of data into reports and interactive dashboards, contributing to decision-making within the department.

Junior Actuary

Full-stack Engineer

• Design, develop, and maintain scalable, reliable, and monitorable data pipelines.
• Ensure end-to-end visibility into data flows.
• Trace complex flows to identify weaknesses, bottlenecks, and root causes of structural failures in applications.
• Respond to and proactively prevent incidents.
• Design telemetry, automation, and application cataloging solutions.
• Develop internal solutions using Python and/or Java.
• Integrate data platforms with monitoring and observability tools.
• Provide second-level (L2) support for diagnosing and resolving complex production incidents.
• Analyze infrastructure and application behavior to identify architectural weaknesses and single points of failure.
• Lead and participate in root cause analyses and postmortems.
• Ensure high availability, scalability, and system resilience.
• Apply SRE and resilience engineering practices.
• Design and implement observability strategies using metrics, logs, and distributed tracing.
• Ensure end-to-end traceability across microservices, data platforms, and APIs.
• Develop and maintain dashboards and intelligent alerting systems.
• Define, monitor, and ensure compliance with SLAs, SLOs, and SLIs.
• Develop internal tools, scripts, and telemetry exporters in Python and/or Java.
• Automate infrastructure, deployments, and monitoring configurations through CI/CD using GitHub Actions.
• Integrate SQS and event processing with operational alerts and self-healing mechanisms.
• Work with Datadog, Grafana, Prometheus, and AWS CloudWatch.
• Maintain application and service catalogs, including ownership, dependencies, and topology.
• Document monitoring architectures, runbooks, performance metrics, and network/application topology.
• Promote SRE, automation, and observability practices across engineering and development teams.
• Support the evolution of enterprise standards for resilient architecture and monitoring.

Data Engineering Specialist

Senior Data Analyst

Data Analyst, Mid-Level

Junior Actuary

Software Engineer I