Our Data Engineering team is the analytical heart of the company. We are responsible for building, optimizing, and maintaining a robust and scalable infrastructure that transforms raw data into strategic assets ready for consumption by business units, data science teams, and managerial reporting. We operate in a 100% Google Cloud Platform (GCP) cloud environment, using BigQuery as our next-generation primary data warehouse. We work closely with product, BI, and analytics teams to understand information needs and design solutions that ensure data quality, performance, and governance. Our day-to-day activities involve managing complex ETL/ELT pipelines—from ingestion from diverse sources (internal and external) to final delivery into optimized datasets. For us, data engineering is not just about moving bytes—it’s about enabling data-driven decision-making across the entire organization. Additionally, we value autonomy, innovation, and continuous pursuit of cost and performance optimization within the GCP ecosystem. If you’re looking for a place where your work has direct business impact and where BigQuery is the star, this is your place. **Responsibilities and Duties** The Data Engineer will play a pivotal role in evolving and maintaining our data ecosystem, with full focus on BigQuery and the GCP ecosystem. Key responsibilities include: * **Design and Implementation of Data Pipelines:** Design, build, and maintain scalable data pipelines (ETL/ELT) using technologies such as Cloud Dataflow (Apache Beam), Cloud Composer (Apache Airflow), or Cloud Functions for batch and streaming data ingestion into BigQuery. * **BigQuery Performance and Cost Optimization:** Lead analysis and optimization of schemas, partitioning, clustering, and complex SQL queries in BigQuery, ensuring high performance and strict control over storage and query costs. * **Data Modeling:** Define and implement robust data models (e.g., Data Vault, Dimensional) in BigQuery to meet business intelligence and machine learning requirements, ensuring data integrity and quality. * **Data Governance and Quality (Data Quality):** Implement monitoring, alerting, and validation mechanisms to guarantee accuracy, completeness, and timeliness of critical datasets, leveraging GCP tools (e.g., Data Catalog or custom solutions). * **Mentoring and Technical Leadership:** Serve as a technical reference for junior and mid-level engineers—reviewing code, defining engineering standards, and disseminating best practices across the GCP/BigQuery ecosystem. * **Documentation:** Ensure all architecture, data models, and pipelines are properly documented and versioned. In addition, the professional will tackle strategic and technical challenges critical to scaling and improving the efficiency of our data environment, such as: * **Massive BigQuery Cost and Performance Optimization:** The biggest challenge is balancing growing data demand with intelligent management of BigQuery resources. This involves: rewriting inefficient historical queries, designing schemas for minimal data scanning, and implementing advanced slot usage strategies to maintain performance control—ensuring the lowest possible per-unit cost per served data item. * **Unification and Governance of Heterogeneous Data Sources:** We have a variety of legacy systems and microservices generating data in different formats (JSON, CSV, transactional databases, logs). The challenge is standardizing timely ingestion, applying rigorous data quality rules in Dataflow/Airflow before persistence, and building a unified data catalog (Data Catalog) to ensure trust in reported metrics. * **Transition from Batch to Hybrid Streaming Pipelines:** Evolve the current batch-scheduling architecture (Airflow) into one supporting low-latency streaming ingestion (Pub/Sub and Dataflow) for mission-critical data—guaranteeing atomicity and exactly-once delivery without compromising robustness or causing rework for downstream consumers. * **Support for Machine Learning (ML) Use Cases:** Build feature stores and ML-ready datasets, ensuring training and scoring data remain fresh, free of data drift, and optimized for consumption by the Data Science team (direct integration with Vertex AI ecosystem), accelerating the model lifecycle. * **Maintaining IaC (Infrastructure as Code) at Scale:** As our company grows, our data infrastructure expands. The challenge is ensuring 100% of resources (BigQuery datasets, Dataflow jobs, Cloud Storage buckets) are managed via Terraform and versioned—keeping environments consistent, auditable, and enabling rapid replication across development and staging environments. **Requirements and Qualifications** We seek a professional with proven experience and deep knowledge of cloud-based data ecosystems, with BigQuery as the central pillar. **Mandatory Requirements:** * **Proven Experience:** Minimum of 3 years of relevant experience in Data Engineering. * **BigQuery Proficiency:** Advanced experience in SQL query optimization, slot management, partitioning, clustering, materialized views, and deep understanding of BigQuery’s cost model and internal architecture. * **Python Proficiency:** Ability to develop robust, efficient, and testable code for data manipulation and pipeline construction. * **Google Cloud Platform (GCP):** Practical experience with at least three of the following tools: Cloud Dataflow (Apache Beam), Cloud Composer (Apache Airflow), Cloud Storage, Cloud Pub/Sub, Cloud Functions. * **Data Modeling:** Knowledge and application of various dimensional modeling techniques (e.g., Star Schema, Snowflake) and/or Data Vault. * **Automation and Infrastructure:** Experience with Terraform or another IaC tool, and familiarity with Git and CI/CD pipelines. * **DataOps and Monitoring:** Experience implementing data quality tests and pipeline monitoring in production environments. * **Communication:** Ability to explain complex technical concepts to non-technical audiences and influence architectural decisions. **Desired Qualifications:** * Google Cloud certification (e.g., Professional Data Engineer). * Experience with stream processing tools (e.g., Kafka, Pub/Sub). * Knowledge of Machine Learning Engineering and integration with Vertex AI. * Knowledge of Gemini Enterprise. **Additional Information** **Work Location:** Barra Funda – São Paulo/SP **Work Model:** Fixed on-site presence during the first months, followed by the possibility of hybrid work. **Business Hours:** Availability from 8:00 AM to 6:30 PM **Work Schedule:** 5x2 – Monday to Friday **Benefits:** **Flexible Meal and Food Allowance;** **Health Insurance Plan;** **Dental Insurance Plan;** **Wellhub and TotalPass;** **Profit Sharing Program (PLR);** **Stock Option Program: Porto em Ação:** complementary to PLR until 2026; **Transportation Allowance;** **Extended Parental Leave:** up to 40 days for all family configurations; **Extended Maternity Leave** of 6 months; **Medical Outpatient Clinic with Specialties:** located at Complexo Matriz and Barra Funda; **Childcare or Babysitter Assistance;** **Life Insurance;** **Private Pension Plan – PortoPrev;** **Discounts on Products and Services;** **Scholarship Program: Reimbursement for undergraduate, graduate, or MBA studies;** **Monthly Running Events:** subsidy for major street races in São Paulo; **Language Reimbursement (English or Spanish);** **Teatro Porto:** exclusive sessions for Employees. Porto is far more than an insurance company: it is a massive ecosystem leveraging technology to create solutions across four business verticals: **Porto Bank, Porto Saúde, Porto Seguro, and Porto Serviço**. Its reputation and recognition earned over more than seven decades underscore Porto’s scale—a business reference built by and for people. Here, 13,000 employees proudly wear their hearts on their sleeves—**proud to be part of a solid, ethical organization** that **transforms dreams into fantastic realities** for our 15.8 million+ customers. We operate through over 101 branches and regional offices across Brazil. Learning is embedded in our DNA—and here, you’ll find **incentives, tools, and great people to help you learn and grow every day**, in a welcoming and secure environment where everyone can truly be themselves and take pride in who they are—fostering respect and **living fully and well-being in a genuinely human company.** **DIVERSITY & INCLUSION AT PORTO** At Porto, we value diversity and believe a plural team is essential for developing our entire business, society, and each individual—because we understand that every person is unique in building a more innovative and inclusive environment. Together, we work daily to build a safe, free, and welcoming space—with greater plurality and respect. Therefore, **we strongly encourage applications from women, Black and Indigenous people, people with disabilities, and LGBTQIA+ individuals** for all our open positions.
João Silva
Indeed · HR
João Silva
Indeed · HR
