This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Engenheiro de Dados Sênior/Data Architecture based in Brazil.
This is a senior-level opportunity for a Data Engineer who combines strong hands-on engineering skills with architectural ownership.
You will help design, implement, and evolve analytical data platforms supporting high-impact and business-critical environments.
The role goes beyond development, requiring you to make architectural decisions, optimize data workloads, and ensure reliable production operations.
You will work extensively with SQL, Python, Airflow, cloud data warehouses, dbt, dimensional modeling, and modern development practices.
There is significant opportunity to influence data architecture, engineering standards, performance, scalability, and migration initiatives.
Experience in financial services is particularly valuable, especially where regulatory, investment, or high-criticality data requirements are involved.
The environment favors technical autonomy, collaboration, continuous learning, and professionals who want to build modern data solutions with measurable business impact.
Accountabilities:
- Design and evolve scalable, reliable, and maintainable data architectures, taking responsibility for architectural decisions rather than focusing solely on implementation.
- Develop and operate analytical data platforms in production, ensuring reliability, availability, performance, and operational resilience.
- Build robust data pipelines using Python, Apache Airflow, SQL, dbt, and cloud-based data warehouse technologies.
- Develop and maintain Airflow DAGs, sensors, dependencies, backfills, failure handling, and production workflows.
- Design dimensional data models following Kimball principles, including fact and dimension tables, appropriate granularity, SCD Types 1 and 2, bridge tables, and snapshots.
- Analyze and optimize complex SQL workloads, including execution plans, query performance, skew, disk spills, and other causes of processing bottlenecks.
- Establish and validate performance improvements through measurable before-and-after metrics.
- Apply software engineering best practices to data projects, including modular and testable code, version control, automated testing, code reviews, and CI/CD.
- Collaborate with engineering, product, business, and technology stakeholders to translate business needs into scalable data solutions.
- Contribute to the evolution of cloud data architectures and modern data platforms, with particular focus on AWS and Amazon Redshift.
- Support high-impact data migrations, including legacy-to-modern platforms, on-premises-to-cloud environments, and spreadsheet/VBA-based processes to versioned data pipelines.
- Ensure data quality, consistency, and parity during migrations and platform transformations.
- Participate in production incident management, troubleshooting, and on-call activities when required.
- Contribute to initiatives involving data governance, observability, quality, cataloging, semantic layers, and modern analytics capabilities.
- When applicable, support streaming and change data capture architectures using technologies such as Kafka, Debezium, or Kinesis.
Requirements:
- 6+ years of professional experience in Data Engineering, including at least 2 years with architectural responsibilities and ownership of solution design.
- Proven experience implementing and operating analytical data platforms in production environments, including incident management, on-call responsibilities, and high-criticality workloads.
- Advanced SQL skills, including Window Functions, recursive CTEs, execution plan analysis, skew diagnosis, and disk-spill troubleshooting.
- Strong Python experience applied to Data Engineering, with the ability to develop modular, testable, version-controlled code.
- Familiarity with Pandas or Polars, typing, automated testing, and modern Python development practices.
- Strong production experience with Apache Airflow, including DAG development, sensors, backfills, dependency management, and failure handling.
- Experience with cloud-based MPP Data Warehouses, preferably Amazon Redshift. Knowledge of Snowflake, BigQuery, or Databricks is also valuable, with willingness to deepen expertise in Redshift.
- Experience with dbt or equivalent tools for version-controlled data transformation and automated testing.
- Strong command of dimensional modeling and Kimball methodology, including fact and dimension concepts, granularity, SCD Types 1 and 2, bridge tables, and snapshots.
- Experience with Git, code review, collaborative development practices, and CI/CD for data projects.
- Strong data performance modeling and troubleshooting capabilities, with the ability to identify query bottlenecks and demonstrate optimization results using measurable metrics.
- Strong analytical thinking, problem-solving ability, technical ownership, and communication skills.
- Experience in financial services is a strong advantage, particularly across investments, wealth management, custody, fixed and variable income, funds, profitability calculations, positions, and transactions.
- Familiarity with regulatory data and requirements involving CVM, BACEN, BSM, or ANBIMA is a plus.
- Experience with AWS services such as S3, Glue, Lambda, IAM, and Step Functions, as well as Infrastructure as Code with Terraform, is desirable.
- Knowledge of open table formats such as Iceberg and Delta and modern Lakehouse architectures is a plus.
- Experience with Streaming and CDC technologies such as Kafka, Debezium, or Kinesis is desirable.
- Familiarity with data quality and cataloging tools such as Great Expectations, Soda, DataHub, or OpenMetadata is advantageous.
- Experience with semantic layers and BI platforms such as Power BI, Metabase, or Looker is a plus.
- Experience leading significant data migrations, including legacy modernization, on-premises-to-cloud transformations, or spreadsheet/VBA-to-versioned-pipeline initiatives, is highly valued.
Benefits:
- Flash Card: R$ 936 per month with flexible use for food, meals, and mobility.
- SulAmérica Health Plan: no co-payment or employee discount.
- SulAmérica Dental Plan: no co-payment or employee discount.
- Life insurance.
- Gympass | Wellhub: access to gyms and physical activities.
- TotalPass: access to gyms, sports, wellness, and physical activities.
- SESC partnership: benefits across culture, leisure, sports, and well-being.
- Creditas: access to financial benefits, loans, and pension options.
- Continuous knowledge exchange with technology professionals.
- Opportunities for repositioning into new internal projects.
- Direct interaction with leadership and clients.
- Close support from People and Business Partner teams.
- Contract: CLT or PJ.
- Work model: Remote, with São Paulo-based opportunities.
- Opportunity to contribute to modern data architecture initiatives and high-impact technology projects.
- Professional environment focused on technical growth, collaboration, and continuous learning.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
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