Engenheiro de Dados Sênior (Redshift, Airflow e DBT)

Accountabilities:

• • Design, build, and maintain production-grade data ingestion and transformation pipelines orchestrated with Apache Airflow, applying consistent standards for retries, idempotency, alerts, dependencies, and SLAs.
  • Develop and evolve dbt transformation models, organizing staging, intermediate, and mart layers with appropriate testing, documentation, and version control.
  • Implement ingestion pipelines for heterogeneous sources, including transactional databases, third-party APIs, regulatory files, and operational spreadsheets.
  • Define and evolve the data warehouse architecture, selecting appropriate dimensional, Data Vault, or hybrid modeling approaches and establishing keys, granularity, historization, and handling of retroactive corrections.
  • Establish and enforce MPP warehouse standards covering distribution, sorting, partitioning, compression, and related optimization practices, particularly within Amazon Redshift.
  • Lead database and query performance optimization by analyzing execution plans, workload management, concurrency, maintenance requirements, query rewrites, and materializations while balancing performance and cost.
  • Implement data observability and automated quality controls covering freshness, volume, data contracts, source-to-target reconciliation, and other critical reliability indicators.
  • Define data layers and contracts between teams, establishing sources of truth, ownership boundaries, and the data exposed to BI and downstream consumers.
  • Ensure end-to-end data lineage and traceability, allowing business metrics to be connected back to their source and the code version that produced them.
  • Apply appropriate access controls, data segregation, and governance practices aligned with LGPD and relevant industry requirements.
  • Document architectural decisions, maintain a living data dictionary, participate in code reviews and pair programming, and establish sustainable engineering standards.
  • Act as a technical reference for other engineers through mentoring, knowledge sharing, architectural guidance, and constructive technical reviews.
  • Translate business requirements into sustainable data solutions, proactively identifying risks and avoiding shortcuts that create unnecessary technical debt.

  Requirements

  • 6+ years of professional experience in Data Engineering, including at least 2 years with architectural responsibility and ownership of data platform design decisions.
  • Proven experience deploying and operating analytical data platforms in production, including experience handling incidents, operational responsibilities, and real-world reliability requirements.
  • Advanced SQL skills, including window functions, recursive CTEs, execution-plan analysis, and diagnosis of skew and disk spill.
  • Strong Python experience applied to data engineering, with modular, testable, version-controlled code, including experience with pandas or Polars, typing, and automated testing.
  • Hands-on production experience with Apache Airflow, including DAG development, sensors, backfills, dependency management, and failure handling.
  • Experience with a cloud-based MPP data warehouse, preferably Amazon Redshift; experience with Snowflake, BigQuery, or Databricks is also relevant, provided there is willingness to deepen Redshift expertise.
  • Practical experience with dbt or an equivalent version-controlled transformation framework, including automated testing.
  • Strong knowledge of dimensional modeling/Kimball, including fact and dimension tables, granularity, SCD Types 1 and 2, bridge tables, and snapshots.
  • Experience with Git, code review, and CI/CD practices applied to data engineering.
  • Proven ability to diagnose and optimize performance, clearly explaining the cause of slow queries and demonstrating improvements with measurable before-and-after results.
  • Strong attention to numerical accuracy and data correctness, particularly in environments where results must be reliable, reproducible, and auditable.
  • Clear written and verbal communication skills, with the ability to document architectural decisions and explain complex technical concepts to both technical and business stakeholders.
  • High degree of autonomy and investigative ability, including tracing data issues from dashboards and outputs back to their original sources.
  • Demonstrated ability to mentor engineers, conduct code reviews, pair with colleagues, and act as a technical reference.
  • Ability to translate business needs into sustainable data architectures while constructively challenging approaches that may introduce technical debt.
  • Differentiators: experience in financial markets, investments, wealth management, custody, fixed or variable income, funds, profitability calculations, or regulatory data from organizations such as CVM, BACEN, BSM, or ANBIMA.
  • Additional valuable experience includes Terraform, AWS services such as S3, Glue, Lambda, IAM and Step Functions, Iceberg or Delta, lakehouse architectures, Kafka, Debezium, Kinesis, data-quality/catalog tools, semantic layers, and BI platforms.
  • Experience leading significant data migrations, such as legacy-to-modern platforms, on-premises-to-cloud transformations, or replacing spreadsheet/VBA-based processes with version-controlled pipelines.

  Benefits

  • 100% remote work, offering flexibility to work from anywhere in Brazil.
  • Opportunity to work on complex data engineering and architecture challenges in a technology-focused environment.
  • Exposure to critical data platforms supporting business, client reporting, and regulatory requirements.
  • Opportunities to act as a technical reference, mentor other engineers, and contribute to architectural decisions.
  • Continuous learning and development in Data Engineering, Cloud, Analytics, and AI.
  • Collaborative environment focused on technical excellence, innovation, ethics, transparency, teamwork, and professional growth.
  • Opportunity to work with modern data technologies including Redshift, Airflow, dbt, Python, SQL, and cloud platforms.