Engenheiro de Dados Sênior (Redshift, Airflow e DBT)

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 (Redshift, Airflow e DBT) based in Brazil.

This is a senior-level opportunity for a Data Engineer who wants to combine hands-on engineering with architectural ownership.

You will design, build, and operate reliable data platforms supporting critical business and regulatory needs.

The role covers data warehouse modeling, ingestion standards, transformation layers, governance, performance, and cost optimization.

You will work extensively with Amazon Redshift, Apache Airflow, dbt, SQL, and Python in a cloud-based environment.

Your work will ensure that data powering executive dashboards, client reporting, and regulatory obligations is accurate, traceable, and auditable.

You will collaborate with business and technology teams while serving as a technical reference for other data engineers.

This is a fully remote position in a demanding environment where engineering rigor, autonomy, and data quality are highly valued.

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.