Senior Data Engineer Brazil

We are seeking a talented and experienced Senior Data Engineer to join our team and support a strategic partner in the evolution, maintenance, and continuous improvement of global financial closing processes.

This role will focus on supporting financial data reconciliation, P&L routines, existing data pipelines, and the integration of new data sources. The professional will also contribute to building new pipelines, improving data quality and support initiatives involving AI models and advanced data capabilities.

We are looking for someone hands-on, collaborative, and analytical, who enjoys solving complex data challenges and building reliable, scalable solutions that create real business impact.

Responsibilities

Data Pipeline Development: Develop, maintain, and improve data pipelines using Databricks, PySpark, Python, and SQL to transform raw financial data into reliable curated datasets.

Data Integration: Integrate new data sources into the existing data ecosystem, helping expand the platform’s capabilities and improve business visibility.

Financial Data Reconciliation: Support financial reconciliation activities, P&L routines, and global closing processes by ensuring data consistency, accuracy, and traceability across different sources.

Data Processing and Optimization: Process large volumes of structured and semi-structured data, optimizing transformations, storage formats, and query performance using Delta Lake and Lakehouse best practices.

Pipeline Maintenance and Support: Monitor, troubleshoot, and support existing pipelines, identifying issues, investigating discrepancies, and ensuring reliable daily and monthly processing.

Continuous Improvement: Propose and implement better technical approaches to improve performance, scalability, maintainability, and data quality across the financial data ecosystem.

Collaboration with Cross-functional Teams: Work closely with existing data engineers, business stakeholders, finance teams, and technology teams to understand needs and deliver high-quality data solutions.

Governance and Data Quality: Support data governance, validation rules, auditability, documentation, and controls to ensure trustworthy and well-managed financial data.

AI Integration: Support initiatives involving AI models and advanced data capabilities, helping integrate AI-driven insights and automation into data workflows.

Requirements

Strong experience with Databricks, including notebooks, jobs, workflows, clusters, and best practices for building scalable data solutions.

Proficiency in data processing using PySpark, Python, and SQL, with hands-on experience transforming raw data into curated and reliable datasets.

Experience with Delta Lake and Lakehouse architecture, including Delta tables, incremental processing, data optimization, and structured data layers.

Knowledge of Azure data ecosystem, especially Azure Data Lake Storage, data integration patterns, and cloud-based data processing.

Experience supporting financial data pipelines, preferably involving reconciliation, P&L, financial closing, or other critical business processes.

Hands-on experience maintaining and improving existing pipelines, including troubleshooting, performance tuning, documentation, and production support.

Ability to work with structured and semi-structured data, ensuring data quality, traceability, and readiness for analytical and business consumption.

Problem-solving mindset, with the ability to investigate complex data, understand root causes, and propose practical solutions in challenging data environments.

Interest or experience with AI models and advanced data capabilities, supporting the integration of AI-driven solutions into data workflows.

English proficiency for interaction with global teams, technical discussions, and documentation.

Nice to Have:

Familiarity with AI frameworks and methodologies.

Experience with advanced capabilities, such as Delta Lake optimization, Databricks Workflows and Databricks Apps or Streamlit.

Experience with data quality and observability practices, including validation rules, reconciliation checks, monitoring, logs, alerts, and pipeline execution metrics.