Engenheiro de Dados Sênior/Data Architecture

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.