Top Data Engineer Jobs in Rio de Janeiro

Designs scalable data architectures, ELT/ETL pipelines, data models, quality frameworks, and self-service data platforms. Partners with engineering, product, program, leadership, and data science teams to translate business needs into technical solutions. The role includes end-to-end technical ownership, large-scale batch and streaming data processing, mentoring junior engineers, system optimization, and on-call support for platform stability.

Own FinOps and data initiatives focused on optimizing Databricks workloads, pipelines, clusters, storage, and cloud costs. Build executive dashboards, monitor spending, detect anomalies, establish governance, and improve showback, chargeback, forecasting, and unit economics. Partner with engineering, finance, product, cloud, and operations teams to guide cost-efficient architecture and ROI-based decisions.

Investigate and resolve enterprise data incidents using SQL and MS SQL Server; monitor Airflow, Azure Data Factory, and SQL Agent pipelines; troubleshoot Tableau dashboards and data warehouses; perform controlled data corrections; automate investigations with Python or PowerShell; and coordinate incident resolution through Jira Service Management with engineering, IT, and business teams.

Build and maintain high-volume ETL/ELT pipelines using Python, SQL, Airflow, Azure Data Factory, and SQL Server/PostgreSQL. Design data ingestion, metadata, chunking, and embedding pipelines for AI and RAG systems. Optimize queries, dimensional models, warehouses, and lakehouses while enforcing data quality and governance. Collaborate with product owners, data scientists, and IT Operations to resolve production issues and maintain Git, Jira, and CI/CD standards.

Build semantic models, analytical views, and production ETL/ELT pipelines; optimize SQL queries and schemas; implement governance, security, data quality, monitoring, and anonymization; and support AI, RAG, and agentic AI workflows. The role requires strong production data engineering experience, dimensional modeling, dbt, database security, query tuning, and advanced English for direct client collaboration.

Develops data integration and analytics solutions for financial loss reporting. Partners with stakeholders, product owners, SMEs, and technology teams to document requirements, validate business rules, support source-to-target mapping and UAT, certify data products, resolve data quality issues, and improve dashboards, reports, data platforms, and downstream data consumption.

Designs, develops, and supports scalable data pipelines, integrations, data models, warehouses, and enterprise data products. The role integrates internal, cloud, API, and vendor data while ensuring quality, governance, performance, and reliability. Responsibilities include stakeholder collaboration, technical leadership, Agile project support, vendor partnership, and continuous improvement of data architecture and platform capabilities.

Build and own an enterprise lakehouse data platform from the ground up, including architecture, ingestion, transformation, entity resolution, pipelines, infrastructure as code, and platform services. Establish engineering standards, mentor team members, and ensure the platform supports analytics, machine learning, and business intelligence. Partner directly with client leadership to translate business needs into technical solutions and present architectural decisions.

Build, maintain, monitor, and improve high-performance data pipelines across Azure and on-premises platforms. Design ETL/ELT processes, data lakes, data marts, and warehouses while ensuring performance, security, privacy, and reliability. Collaborate with BI, data science, and software engineering teams, contribute to data standards and code reviews, support incident resolution, and apply software engineering practices including Git and CI/CD. Mentor junior team members and integrate emerging data technologies.

Design, build, and optimize high-throughput batch and streaming data pipelines, cloud data warehouses, and lakehouse architectures for financial and transactional data. Implement data quality, testing, monitoring, DataOps, CI/CD, infrastructure as code, governance, privacy, and security practices. Collaborate with technical and product teams, ensure reliable and compliant production systems, and mentor junior and mid-level data engineers.

Design and optimize Snowflake data models, pipelines, and customer data foundations for CRM, personalization, segmentation, and audience activation. Build customer 360 views, identity resolution frameworks, CDP-ready schemas, and integrations with Braze, Hightouch, and related platforms. Ensure performance, data quality, governance, consent compliance, and scalability while partnering with CRM, marketing, analytics, and technical stakeholders.

Build and manage Databricks ingestion and reconciliation pipelines using medallion architecture, Auto Loader, Delta Lake, and PySpark. Model reporting data, expose results through APIs, configure Unity Catalog governance, orchestrate workflows, implement data quality checks, and manage version control with Asset Bundles. The role also requires advanced SQL and Python, SAP data knowledge, migration validation experience, reporting development, and advanced conversational English.

Build and govern production-grade data foundations in Databricks, including entity reconciliation, history-aware data models, provenance, lineage, quality testing, incremental processing, and access controls. Develop APIs and data services with permission enforcement, collaborate with client data teams and stakeholders, and independently resolve conflicting definitions and identifiers. Use Python, SQL, Spark, and AI coding tools while ensuring reliable, secure, and well-documented data for applications, AI agents, and business users.

Design and optimize Snowflake data models and dbt transformation workflows, modernize Oracle ETL processes, build Python automation, and support scalable data architecture. The role involves troubleshooting data quality and performance issues, contributing to DevOps and deployment activities, owning technical deliverables, and participating in client-facing discussions and solution design.

Designs and deploys secure AWS cloud architectures, leads enterprise migrations and application modernization, and builds Generative AI and machine learning solutions using Bedrock, SageMaker, vector databases, and RAG. Acts as the primary technical leader for client engagements, advises executive stakeholders, develops proofs of concept, drives AWS adoption and account growth, and mentors engineering teams while creating reusable infrastructure and delivery frameworks.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

Design, build, maintain, and optimize scalable ETL pipelines using PySpark and Databricks. Responsibilities include complex data transformations, performance and cloud cost optimization, troubleshooting pipeline bottlenecks, enforcing data quality and governance standards, and collaborating with technical and business teams to deliver production-grade data solutions.

Design, build, maintain, and optimize scalable ETL pipelines using PySpark and Databricks. Develop complex data transformations, improve performance and cloud cost efficiency, troubleshoot processing bottlenecks, and enforce data quality and governance standards. Collaborate with engineering and business teams to deliver production-grade data solutions and communicate technical concepts clearly.

Own and evolve databases, storage, networking, and Kubernetes infrastructure as platform products. Responsibilities include provisioning, backups, upgrades, capacity planning, performance tuning, migrations, incident leadership, root-cause analysis, on-call participation, and roadmap ownership. The role requires operating high-throughput ClickHouse or comparable OLAP systems, Ceph or S3-compatible storage, self-managed Kubernetes on bare metal, Linux performance debugging, and production networking expertise.

Build and optimize cloud-agnostic Databricks Lakehouse architectures and scalable ETL/ELT pipelines using Spark and Delta Lake. Integrate MLflow for AI model operationalization, implement CI/CD and Infrastructure-as-Code deployments, tune performance and cloud costs, and enforce data governance and security. Collaborate with architects, data scientists, engineering teams, and clients to deliver production-ready AI solutions and technical handoffs.

Build and maintain curated data layers for a finance data platform using SQL and Snowflake. Develop dimensional models, business transformations, Slowly Changing Dimensions, reconciliation checks, data-quality controls, and temporal data logic. Support BI consumers through governed data objects and participate in code reviews, CI/CD promotion, and automated testing. Collaborate in English with distributed engineering teams and US stakeholders.

Build and operate production data infrastructure, including batch and streaming pipelines, warehouses and lakehouses, retrieval systems, and data-quality controls. Design for schema evolution, reliability, cost, security, compliance, and observability using cloud platforms and modern orchestration tools. Partner directly with clients, explain technical trade-offs, and deploy containerized systems with CI/CD and infrastructure as code. Support AI applications through embedding, indexing, and retrieval pipelines.

Owns operational readiness for a production cloud data platform, including environment provisioning, CI/CD, multi-environment promotion, automated regression testing, monitoring, alerting, cost governance, runbooks, and client knowledge transfer. The role supports lakehouse, cloud warehouse, and CDC replication operations, with Snowflake preferred and Microsoft Fabric, OneLake, and Qlik Replicate as valuable experience.

Designs, develops, and maintains large-scale Databricks data pipelines using Python, SQL, PySpark, and Delta Lake. Integrates APIs, databases, and files; applies ETL/ELT, DataOps, CI/CD, testing, governance, and data quality practices. Collaborates with business, analytics, data science, and platform teams while mentoring peers and troubleshooting performance, scalability, and reliability issues.

Builds and maintains scalable batch and streaming data pipelines across on-premises and cloud environments. Designs data models, data flows, APIs, integrations, and access methods for reporting, dashboards, operational use, and data science. Translates business requirements into technical solutions, manages code integration, applies data governance, cleanses data, and supports complex project planning and delivery. Works independently across multiple dependencies and provides coaching to junior associates.