Senior Data Engineer, Redshift, Airflow, dbt

Leega

Engenheiro de Dados Sênior projetando pipelines, warehouses e governança para a consultoria de tecnologia Leega. Atuação em dados financeiros regulados, performance, qualidade e rastreabilidade ponta a ponta.

Posted 8/21/2026contractRemote • 🇧🇷 BrazilSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and maintaining data ingestion and transformation pipelines using Apache Airflow and dbt, with a strong focus on performance tuning, compliance with regulations, and collaboration across teams. Proficient in advanced SQL and Python for data engineering, ensuring high-quality data models and observability.

Highest-signal resume keywords

Apache AirflowAdvanced SQLPython Applied To Data EngineeringCloud-Based MPP Data WarehouseDimensional Modeling

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringDAG AuthoringExecution-Plan AnalysisModular CodeVersion-Controlled CodeData TransformationPerformance DiagnosisData Quality ToolsRegulatory ComplianceData Modeling

Soft Skills

Technical LeadershipClear Written CommunicationAutonomyMentoring

Tools & Technologies

DbtRedshiftSnowflakeBigQueryDatabricksGitCI/CDTerraformKafkaKinesis

Industry Keywords

Financial ServicesRegulatory DataData QualityData CatalogLakehouse Architectures

Tech Stack

Tools & technologies

AirflowAmazon RedshiftApacheAWSBigQueryKafkaPandasPythonSQLTerraform

About the role

Key responsibilities & impact

Design, build, and maintain ingestion and transformation pipelines orchestrated with Apache Airflow, including retries, idempotency, alerting, and SLAs
Develop and evolve dbt transformation models on the data warehouse, organizing staging, intermediate, and marts layers with tests and documentation
Implement ingestion from transactional databases, third-party APIs, regulatory files, and operational spreadsheets
Define and champion the warehouse data model, including keys, grain, historization, and retroactive corrections
Establish distribution, sort, partitioning, and compression standards in MPP/Redshift environments
Lead performance and cost tuning by analyzing query plans, queues/WLM, concurrency, VACUUM/ANALYZE, queries, and materializations
Instrument observability and automated quality controls covering freshness, volume, contracts, and reconciliation
Design data layers and contracts across teams, defining the source of truth and exposure for BI
Ensure lineage, traceability, access controls, sensitivity-based segregation, and compliance with Brazil’s LGPD and industry regulations
Document architectural decisions, maintain the data dictionary, and contribute to code reviews, pair programming, and standards definition
Collaborate with Wealth Management, Sales, Risk, Compliance, and Operations
Serve as a technical reference for mid-level and junior engineers

Requirements

What you’ll need

6+ years of experience in data engineering, including at least 2 years with architectural responsibilities
Proven track record of putting an analytical data platform into production and operating it, including incidents, on-call rotations, and real-world consequences
Advanced SQL: window functions, recursive CTEs, execution-plan analysis, and diagnosis of data skew and disk spills
Python applied to data engineering, with modular, testable, version-controlled code, including pandas or polars, type annotations, and automated tests
Apache Airflow in production: DAG authoring, sensors, backfills, dependency management, and failure handling
Cloud-based MPP data warehouse, preferably Redshift; Snowflake, BigQuery, or Databricks also accepted
dbt or an equivalent version-controlled transformation tool, with testing
Dimensional modeling (Kimball), including fact and dimension tables, grain, SCD Types 1 and 2, bridge tables, and snapshots
Git and a code review culture, with CI/CD applied to data
Performance diagnosis, including explaining slow queries and proving fixes with measurable results
Nice to have: experience in financial services, regulatory data, Terraform, AWS services, Iceberg, Delta, lakehouse architectures, Kafka, Debezium, Kinesis, data quality and catalog tools, BI, and significant migrations
Strong numerical accuracy, clear written communication, autonomy, technical leadership, mentoring, and the ability to translate business needs into sustainable data designs

Benefits

Comp & perks

Ongoing professional development
Dynamic and collaborative environment
Leega is an inclusive workplace for everyone
Position also open to candidates with disabilities